diff --git a/.dev_scripts/README.md b/.dev_scripts/README.md index d599a1633a..44d1591e56 100644 --- a/.dev_scripts/README.md +++ b/.dev_scripts/README.md @@ -12,6 +12,7 @@ - [7. Train failed or canceled jobs](#7-train-failed-or-canceled-jobs) - [8. Deterministic training](#8-deterministic-training) - [9. Automatically check links](#9-automatically-check-links) +- [10. Calculate flops](#10-calculate-flops) ## 1. Check UT @@ -235,3 +236,11 @@ python .dev_scripts/doc_link_checker.py --target README.md You can specify the `--target` by a file or a directory. **Notes:** DO NOT use it in CI, because requiring too many http requirements by CI will cause 503 and CI will propabaly fail. + +10. Calculate flops + +To summarize the flops of different models, you can run the following commands: + +```bash +python .dev_scripts/benchmark_valid_flop.py --flops --flops-str +``` diff --git a/.dev_scripts/doc_link_checker.py b/.dev_scripts/doc_link_checker.py index f9fdd4e42e..51a452b4bb 100644 --- a/.dev_scripts/doc_link_checker.py +++ b/.dev_scripts/doc_link_checker.py @@ -1,20 +1,24 @@ # Copyright (c) MegFlow. All rights reserved. +# Copyright (c) OpenMMLab. All rights reserved. # /bin/python3 import argparse import os import re +import requests +from tqdm import tqdm + def make_parser(): parser = argparse.ArgumentParser('Doc link checker') - parser.add_argument( - '--http', default=False, type=bool, help='check http or not ') parser.add_argument( '--target', default='./docs', type=str, help='the directory or file to check') + parser.add_argument( + '--ignore', type=str, nargs='+', default=[], help='input image size') return parser @@ -22,7 +26,6 @@ def make_parser(): def analyze_doc(home, path): - print('analyze {}'.format(path)) problem_list = [] code_block = 0 with open(path) as f: @@ -51,11 +54,31 @@ def analyze_doc(home, path): end = item.find(')') ref = item[start + 1:end] - if ref.startswith('http') or ref.startswith('#'): + if ref.startswith('http'): + if ref.startswith( + 'https://download.openmmlab.com/' + ) or ref.startswith('http://download.openmmlab.com/'): + resp = requests.head(ref) + if resp.status_code == 200: + continue + else: + problem_list.append(ref) + else: + continue + + if ref.startswith('#'): continue + + if ref == '<>': + continue + if '.md#' in ref: - ref = ref[ref.find('#'):] - fullpath = os.path.join(home, ref) + ref = ref[:ref.find('#')] + if ref.startswith('/'): + fullpath = os.path.join( + os.path.dirname(__file__), '../', ref[1:]) + else: + fullpath = os.path.join(home, ref) if not os.path.exists(fullpath): problem_list.append(ref) else: @@ -68,11 +91,16 @@ def analyze_doc(home, path): raise Exception('found link error') -def traverse(target): +def traverse(args): + target = args.target if os.path.isfile(target): analyze_doc(os.path.dirname(target), target) return - for home, dirs, files in os.walk(target): + target_files = list(os.walk(target)) + target_files.sort() + for home, dirs, files in tqdm(target_files): + if home in args.ignore: + continue for filename in files: if filename.endswith('.md'): path = os.path.join(home, filename) @@ -82,4 +110,4 @@ def traverse(target): if __name__ == '__main__': args = make_parser().parse_args() - traverse(args.target) + traverse(args) diff --git a/.dev_scripts/download_models.py b/.dev_scripts/download_models.py index f3349aaf15..1fff57835e 100644 --- a/.dev_scripts/download_models.py +++ b/.dev_scripts/download_models.py @@ -74,9 +74,7 @@ def download(args): model_index.build_models_with_collections() models = OrderedDict({model.name: model for model in model_index.models}) - http_prefix_long = 'https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/' # noqa http_prefix_short = 'https://download.openmmlab.com/mmediting/' - http_prefix_gen = 'https://download.openmmlab.com/mmgen/' # load model list if args.model_list: @@ -109,12 +107,8 @@ def download(args): model_weight_url = model_info.weights - if model_weight_url.startswith(http_prefix_long): - model_name = model_weight_url[len(http_prefix_long):] - elif model_weight_url.startswith(http_prefix_short): + if model_weight_url.startswith(http_prefix_short): model_name = model_weight_url[len(http_prefix_short):] - elif model_weight_url.startswith(http_prefix_gen): - model_name = model_weight_url[len(http_prefix_gen):] elif model_weight_url == '': print(f'{model_info.Name} weight is missing') return None diff --git a/.dev_scripts/test_benchmark.py b/.dev_scripts/test_benchmark.py index ae36afb13b..ed66aa7491 100644 --- a/.dev_scripts/test_benchmark.py +++ b/.dev_scripts/test_benchmark.py @@ -99,16 +99,10 @@ def create_test_job_batch(commands, model_info, args, port, script_name): assert config.exists(), f'{fname}: {config} not found.' http_prefix_short = 'https://download.openmmlab.com/mmediting/' - http_prefix_long = 'https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/' # noqa - http_prefix_gen = 'https://download.openmmlab.com/mmgen/' model_weight_url = model_info.weights - if model_weight_url.startswith(http_prefix_long): - model_name = model_weight_url[len(http_prefix_long):] - elif model_weight_url.startswith(http_prefix_short): + if model_weight_url.startswith(http_prefix_short): model_name = model_weight_url[len(http_prefix_short):] - elif model_weight_url.startswith(http_prefix_gen): - model_name = model_weight_url[len(http_prefix_gen):] elif model_weight_url == '': print(f'{fname} weight is missing') return None diff --git a/configs/aot_gan/README.md b/configs/aot_gan/README.md index d5e7eb0ccb..8c376fc7f5 100644 --- a/configs/aot_gan/README.md +++ b/configs/aot_gan/README.md @@ -22,9 +22,9 @@ State-of-the-art image inpainting approaches can suffer from generating distorte **Places365-Challenge** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :--------------------------------------------------: | :----------------: | :--------: | :---------: | :-----------: | :------: | :---: | :---: | :---------------------: | :-----------------------------------------------------: | -| [AOT-GAN](/configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py) | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 4 (GeForce GTX 1080 Ti) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth) \| [log](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :------------------------------------------------: | :----------------: | :--------: | :---------: | :-----------: | :------: | :---: | :---: | :---------------------: | :-------------------------------------------------------: | +| [AOT-GAN](./aot-gan_smpgan_4xb4_places-512x512.py) | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 4 (GeForce GTX 1080 Ti) | [model](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.json) | More results for different mask area: @@ -84,13 +84,13 @@ You can use the following commands to test a model with cpu or single/multiple G ```shell # cpu test -CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth +CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth # single-gpu test -python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth +python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth # multi-gpu test -./tools/dist_test.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth 8 +./tools/dist_test.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth 8 ``` For more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMEditing). diff --git a/configs/aot_gan/README_zh-CN.md b/configs/aot_gan/README_zh-CN.md index 508936cbf3..abcec11c8c 100644 --- a/configs/aot_gan/README_zh-CN.md +++ b/configs/aot_gan/README_zh-CN.md @@ -20,9 +20,9 @@ **Places365-Challenge** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :------------------------------------------------------: | :----------------: | :-----: | :--------: | :-----------: | :-----: | :---: | :---: | :---------------------: | :------------------------------------------------------: | -| [AOT-GAN](/configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py) | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 4 (GeForce GTX 1080 Ti) | [模型](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth) \| [日志](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :------------------------------------------------: | :----------------: | :-----: | :--------: | :-----------: | :-----: | :---: | :---: | :---------------------: | :------------------------------------------------------------: | +| [AOT-GAN](./aot-gan_smpgan_4xb4_places-512x512.py) | free-form (50-60%) | 512x512 | 500k | Places365-val | 7.07 | 19.01 | 0.682 | 4 (GeForce GTX 1080 Ti) | [模型](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.json) | @@ -80,13 +80,13 @@ python tools/train.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py ```shell # CPU上测试 -CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth +CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth # 单个GPU上测试 -python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth +python tools/test.py configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth # 多个GPU上测试 -./tools/dist_test.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth 8 +./tools/dist_test.sh configs/aot_gan/aot-gan_smpgan_4xb4_places-512x512.py https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth 8 ``` 更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。 diff --git a/configs/aot_gan/metafile.yml b/configs/aot_gan/metafile.yml index e0cb08db51..f4810ed5df 100644 --- a/configs/aot_gan/metafile.yml +++ b/configs/aot_gan/metafile.yml @@ -20,4 +20,4 @@ Models: SSIM: 0.682 l1 error: 7.07 Task: Inpainting - Weights: https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth + Weights: https://download.openmmlab.com/mmediting/inpainting/aot_gan/AOT-GAN_512x512_4x12_places_20220509-6641441b.pth diff --git a/configs/basicvsr/README.md b/configs/basicvsr/README.md index 7eceedeeb1..2516443664 100644 --- a/configs/basicvsr/README.md +++ b/configs/basicvsr/README.md @@ -23,11 +23,11 @@ Video super-resolution (VSR) approaches tend to have more components than the im Evaluated on RGB channels for REDS4 and Y channel for others. The metrics are `PSNR` / `SSIM` . The pretrained weights of SPyNet can be found [here](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth). -| Method | REDS4 (BIx4) PSNR (RGB) | Vimeo-90K-T (BIx4) PSNR (Y) | Vid4 (BIx4) PSNR (Y) | UDM10 (BDx4) PSNR (Y) | Vimeo-90K-T (BDx4) PSNR (Y) | Vid4 (BDx4) PSNR (Y) | REDS4 (BIx4) SSIM (RGB) | Vimeo-90K-T (BIx4) SSIM (Y) | Vid4 (BIx4) SSIM (Y) | UDM10 (BDx4) SSIM (Y) | Vimeo-90K-T (BDx4) SSIM (Y) | Vid4 (BDx4) SSIM (Y) | GPU Info | Download | -| :--------------------------------------------------------------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :----------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| [basicvsr_reds4](/configs/basicvsr/basicvsr_2xb4_reds4.py) | **31.4170** | 36.2848 | 27.2694 | 33.4478 | 34.4700 | 24.4541 | **0.8909** | 0.9395 | 0.8318 | 0.9306 | 0.9286 | 0.7455 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) | -| [basicvsr_vimeo90k_bi](/configs/basicvsr/basicvsr_2xb4_vimeo90k-bi.py) | 30.3128 | **37.2026** | **27.2755** | 34.5554 | 34.8097 | 25.0517 | 0.8660 | **0.9451** | **0.8248** | 0.9434 | 0.9316 | 0.7636 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) | -| [basicvsr_vimeo90k_bd](/configs/basicvsr/basicvsr_2xb4_vimeo90k-bd.py) | 29.0376 | 34.6427 | 26.2708 | **39.9953** | **37.5501** | **27.9791** | 0.8481 | 0.9335 | 0.8022 | **0.9695** | **0.9499** | **0.8556** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) | +| Method | REDS4 (BIx4) PSNR (RGB) | Vimeo-90K-T (BIx4) PSNR (Y) | Vid4 (BIx4) PSNR (Y) | UDM10 (BDx4) PSNR (Y) | Vimeo-90K-T (BDx4) PSNR (Y) | Vid4 (BDx4) PSNR (Y) | REDS4 (BIx4) SSIM (RGB) | Vimeo-90K-T (BIx4) SSIM (Y) | Vid4 (BIx4) SSIM (Y) | UDM10 (BDx4) SSIM (Y) | Vimeo-90K-T (BDx4) SSIM (Y) | Vid4 (BDx4) SSIM (Y) | GPU Info | Download | +| :----------------------------------------------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :----------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | **31.4170** | 36.2848 | 27.2694 | 33.4478 | 34.4700 | 24.4541 | **0.8909** | 0.9395 | 0.8318 | 0.9306 | 0.9286 | 0.7455 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) | +| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | 30.3128 | **37.2026** | **27.2755** | 34.5554 | 34.8097 | 25.0517 | 0.8660 | **0.9451** | **0.8248** | 0.9434 | 0.9316 | 0.7636 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) | +| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | 29.0376 | 34.6427 | 26.2708 | **39.9953** | **37.5501** | **27.9791** | 0.8481 | 0.9335 | 0.8022 | **0.9695** | **0.9499** | **0.8556** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) | ## Quick Start diff --git a/configs/basicvsr/README_zh-CN.md b/configs/basicvsr/README_zh-CN.md index 11c8a62f2c..69498cb82a 100644 --- a/configs/basicvsr/README_zh-CN.md +++ b/configs/basicvsr/README_zh-CN.md @@ -25,9 +25,9 @@ SPyNet 的 预训练权重在[这里](https://download.openmmlab.com/mmediting/r | 算法 | REDS4 (BIx4)
PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4)
PSNR/SSIM (Y) | Vid4 (BIx4)
PSNR/SSIM (Y) | UDM10 (BDx4)
PSNR/SSIM (Y) | Vimeo-90K-T (BDx4)
PSNR/SSIM (Y) | Vid4 (BDx4)
PSNR/SSIM (Y) | GPU 信息 | 下载 | | :-: | :-----------------------------: | :---------------------------------: | :--------------------------: | :---------------------------: | :---------------------------------: | :--------------------------: | :-----: | :--: | -| [basicvsr_reds4](/configs/basicvsr/basicvsr_2xb4_reds4.py) | **31.4170/0.8909** | 36.2848/0.9395 | 27.2694/0.8318 | 33.4478/0.9306 | 34.4700/0.9286 | 24.4541/0.7455 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) | -| [basicvsr_vimeo90k_bi](/configs/basicvsr/basicvsr_2xb4_vimeo90k-bi.py) | 30.3128/0.8660 | **37.2026/0.9451** | **27.2755/0.8248** | 34.5554/0.9434 | 34.8097/0.9316 | 25.0517/0.7636 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) | -| [basicvsr_vimeo90k_bd](/configs/basicvsr/basicvsr_2xb4_vimeo90k-bd.py) | 29.0376/0.8481 | 34.6427/0.9335 | 26.2708/0.8022 | **39.9953/0.9695** | **37.5501/0.9499** | **27.9791/0.8556** | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) | +| [basicvsr_reds4](./basicvsr_2xb4_reds4.py) | **31.4170/0.8909** | 36.2848/0.9395 | 27.2694/0.8318 | 33.4478/0.9306 | 34.4700/0.9286 | 24.4541/0.7455 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-0e599677.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) | +| [basicvsr_vimeo90k_bi](./basicvsr_2xb4_vimeo90k-bi.py) | 30.3128/0.8660 | **37.2026/0.9451** | **27.2755/0.8248** | 34.5554/0.9434 | 34.8097/0.9316 | 25.0517/0.7636 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-d2d8f760.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) | +| [basicvsr_vimeo90k_bd](./basicvsr_2xb4_vimeo90k-bd.py) | 29.0376/0.8481 | 34.6427/0.9335 | 26.2708/0.8022 | **39.9953/0.9695** | **37.5501/0.9499** | **27.9791/0.8556** | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-0154dd64.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) | ## 快速开始 diff --git a/configs/basicvsr_pp/README.md b/configs/basicvsr_pp/README.md index 807f0a7aca..b81707f718 100644 --- a/configs/basicvsr_pp/README.md +++ b/configs/basicvsr_pp/README.md @@ -24,15 +24,15 @@ The pretrained weights of SPyNet can be found [here](https://download.openmmlab. | Method | REDS4 (BIx4) PSNR (RGB) | Vimeo-90K-T (BIx4) PSNR (Y) | Vid4 (BIx4) PSNR (Y) | UDM10 (BDx4) PSNR (Y) | Vimeo-90K-T (BDx4) PSNR (Y) | Vid4 (BDx4) PSNR (Y) | GPU Info | Download | | :-----------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :-------------------: | :--------------------: | -| [basicvsr_plusplus_c64n7_8x1_600k_reds4](/configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **32.3855** | 36.4445 | 27.7674 | 34.6868 | 34.0372 | 24.6209 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) | -| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](/configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 31.0126 | **37.7864** | **27.7882** | 33.1211 | 33.8972 | 23.6086 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) | -| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](/configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 29.2041 | 34.7248 | 26.4377 | **40.7216** | **38.2054** | **29.0400** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) | +| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **32.3855** | 36.4445 | 27.7674 | 34.6868 | 34.0372 | 24.6209 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) | +| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 31.0126 | **37.7864** | **27.7882** | 33.1211 | 33.8972 | 23.6086 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) | +| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 29.2041 | 34.7248 | 26.4377 | **40.7216** | **38.2054** | **29.0400** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) | | Method | REDS4 (BIx4) SSIM (RGB) | Vimeo-90K-T (BIx4) SSIM (Y) | Vid4 (BIx4) SSIM (Y) | UDM10 (BDx4) SSIM (Y) | Vimeo-90K-T (BDx4) SSIM (Y) | Vid4 (BDx4) SSIM (Y) | GPU Info | Download | | :-----------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :-------------------: | :--------------------: | -| [basicvsr_plusplus_c64n7_8x1_600k_reds4](/configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **0.9069** | 0.9411 | 0.8444 | 0.9417 | 0.9244 | 0.7540 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) | -| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](/configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 0.8804 | **0.9500** | **0.8401** | 0.9270 | 0.9195 | 0.7033 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) | -| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](/configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 0.8528 | 0.9351 | 0.8074 | **0.9722** | **0.9550** | **0.8753** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) | +| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **0.9069** | 0.9411 | 0.8444 | 0.9417 | 0.9244 | 0.7540 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) | +| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 0.8804 | **0.9500** | **0.8401** | 0.9270 | 0.9195 | 0.7033 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) | +| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 0.8528 | 0.9351 | 0.8074 | **0.9722** | **0.9550** | **0.8753** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |
NTIRE 2021 checkpoints @@ -41,25 +41,25 @@ Note that the following models are finetuned from smaller models. The training s **NTIRE 2021 Video Super-Resolution** -[basicvsr-pp_c128n25_600k_ntire-vsr](/configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-vsr.py) +[basicvsr-pp_c128n25_600k_ntire-vsr](./basicvsr-pp_c128n25_600k_ntire-vsr.py) [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_vsr_20210311-1ff35292.pth) **NTIRE 2021 Quality Enhancement of Compressed Video - Track 1** -[basicvsr-pp_c128n25_600k_ntire-decompress-track1](/configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track1.py) +[basicvsr-pp_c128n25_600k_ntire-decompress-track1](./basicvsr-pp_c128n25_600k_ntire-decompress-track1.py) [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track1_20210223-7b2eba02.pth) **NTIRE 2021 Quality Enhancement of Compressed Video - Track 2** -[basicvsr-pp_c128n25_600k_ntire-decompress-track2](/configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track2.py) +[basicvsr-pp_c128n25_600k_ntire-decompress-track2](./basicvsr-pp_c128n25_600k_ntire-decompress-track2.py) [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track2_20210314-eeae05e6.pth) **NTIRE 2021 Quality Enhancement of Compressed Video - Track 3** -[basicvsr-pp_c128n25_600k_ntire-decompress-track3](/configs/basicvsr_pp/basicvsr-pp_c128n25_600k_ntire-decompress-track3.py) +[basicvsr-pp_c128n25_600k_ntire-decompress-track3](./basicvsr-pp_c128n25_600k_ntire-decompress-track3.py) [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c128n25_ntire_decompress_track3_20210304-6daf4a40.pth) diff --git a/configs/basicvsr_pp/README_zh-CN.md b/configs/basicvsr_pp/README_zh-CN.md index 8ef47f6ee4..0c4810d3fd 100644 --- a/configs/basicvsr_pp/README_zh-CN.md +++ b/configs/basicvsr_pp/README_zh-CN.md @@ -22,9 +22,9 @@ SPyNet 的 预训练权重在[这里](https://download.openmmlab.com/mmediting/r | 算法 | REDS4 (BIx4) PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4) PSNR/SSIM (Y) | Vid4 (BIx4) PSNR/SSIM (Y) | UDM10 (BDx4) PSNR/SSIM (Y) | Vimeo-90K-T (BDx4) PSNR/SSIM (Y) | Vid4 (BDx4) PSNR/SSIM (Y) | GPU 信息 | Download | | :-----: | :--------------------------: | :------------------------------: | :-----------------------: | :------------------------: | :------------------------------: | :-----------------------: | :---------: | :------------: | -| [basicvsr_plusplus_c64n7_8x1_600k_reds4](/configs/basicvsr_pp/basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **32.3855/0.9069** | 36.4445/0.9411 | 27.7674/0.8444 | 34.6868/0.9417 | 34.0372/0.9244 | 24.6209/0.7540 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) | -| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](/configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 31.0126/0.8804 | **37.7864/0.9500** | **27.7882/0.8401** | 33.1211/0.9270 | 33.8972/0.9195 | 23.6086/0.7033 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) | -| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](/configs/basicvsr_pp/basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 29.2041/0.8528 | 34.7248/0.9351 | 26.4377/0.8074 | **40.7216/0.9722** | **38.2054/0.9550** | **29.0400/0.8753** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) | +| [basicvsr_plusplus_c64n7_8x1_600k_reds4](./basicvsr-pp_c64n7_8xb1-600k_reds4.py) | **32.3855/0.9069** | 36.4445/0.9411 | 27.7674/0.8444 | 34.6868/0.9417 | 34.0372/0.9244 | 24.6209/0.7540 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217-db622b2f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_600k_reds4_20210217_113115.log.json) | +| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bi](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bi.py) | 31.0126/0.8804 | **37.7864/0.9500** | **27.7882/0.8401** | 33.1211/0.9270 | 33.8972/0.9195 | 23.6086/0.7033 | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305-4ef437e2.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bi_20210305_141254.log.json) | +| [basicvsr_plusplus_c64n7_4x2_300k_vimeo90k_bd](./basicvsr-pp_c64n7_4xb2-300k_vimeo90k-bd.py) | 29.2041/0.8528 | 34.7248/0.9351 | 26.4377/0.8074 | **40.7216/0.9722** | **38.2054/0.9550** | **29.0400/0.8753** | 4 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305-ab315ab1.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr_plusplus/basicvsr_plusplus_c64n7_8x1_300k_vimeo90k_bd_20210305_140921.log.json) |
NTIRE 2021 模型权重文件 diff --git a/configs/biggan/README.md b/configs/biggan/README.md index b98288eddd..dde0566bef 100644 --- a/configs/biggan/README.md +++ b/configs/biggan/README.md @@ -40,9 +40,9 @@ Evaluation of our trained BigGAN. | Models | Dataset | FID (Iter) | IS (Iter) | Config | Download | | :------------------------------------------------: | :--------: | :---------------: | :-----------------: | :-------------------------------------------------: | :---------------------------------------------------: | -| BigGAN 32x32 | CIFAR10 | 9.78(390000) | 8.70(390000) | [config](/configs/biggan/biggan_2xb25-500kiters_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth)\|[log](https://download.openmmlab.com/mmgen/biggan/biggan_cifar10_32_b25x2_500k_20210706_171051.log.json) | -| BigGAN 128x128 Best FID | ImageNet1k | **8.69**(1232000) | 101.15(1232000) | [config](/configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth)\|[log](https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) | -| BigGAN 128x128 Best IS | ImageNet1k | 13.51(1328000) | **129.07**(1328000) | [config](/configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth)\|[log](https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) | +| BigGAN 32x32 | CIFAR10 | 9.78(390000) | 8.70(390000) | [config](./biggan_2xb25-500kiters_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth)\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32_b25x2_500k_20210706_171051.log.json) | +| BigGAN 128x128 Best FID | ImageNet1k | **8.69**(1232000) | 101.15(1232000) | [config](./biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth)\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) | +| BigGAN 128x128 Best IS | ImageNet1k | 13.51(1328000) | **129.07**(1328000) | [config](./biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth)\|[log](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_1500k_20211111_122548-5315b13d.log.json) | | Note: `BigGAN-Deep` trained on `ImageNet1k` will come later. | | | | | | ### Note on reproducibility @@ -57,10 +57,10 @@ Evaluation results and download links are provided below. | Models | Dataset | FID | IS | Config | Download | Original Download link | | :-----------------: | :--------: | :-----: | :-----: | :--------------------------------------------: | :----------------------------------------------: | :-------------------------------------------------------------: | -| BigGAN 128x128 | ImageNet1k | 10.1414 | 96.728 | [config](/configs/biggan/biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth) | [link](https://drive.google.com/open?id=1nAle7FCVFZdix2--ks0r5JBkFnKw8ctW) | -| BigGAN-Deep 128x128 | ImageNet1k | 5.9471 | 107.161 | [config](/configs/biggan/biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin) | -| BigGAN-Deep 256x256 | ImageNet1k | 11.3151 | 135.107 | [config](/configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin) | -| BigGAN-Deep 512x512 | ImageNet1k | 16.8728 | 124.368 | [config](/configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py) | [model](https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin) | +| BigGAN 128x128 | ImageNet1k | 10.1414 | 96.728 | [config](./biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth) | [link](https://drive.google.com/open?id=1nAle7FCVFZdix2--ks0r5JBkFnKw8ctW) | +| BigGAN-Deep 128x128 | ImageNet1k | 5.9471 | 107.161 | [config](./biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-128-pytorch_model.bin) | +| BigGAN-Deep 256x256 | ImageNet1k | 11.3151 | 135.107 | [config](./biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-256-pytorch_model.bin) | +| BigGAN-Deep 512x512 | ImageNet1k | 16.8728 | 124.368 | [config](./biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py) | [model](https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth) | [link](https://s3.amazonaws.com/models.huggingface.co/biggan/biggan-deep-512-pytorch_model.bin) | Sampling results are shown below. diff --git a/configs/biggan/metafile.yml b/configs/biggan/metafile.yml index d70d3070c7..ba91aad6c5 100644 --- a/configs/biggan/metafile.yml +++ b/configs/biggan/metafile.yml @@ -16,7 +16,7 @@ Models: - Dataset: Others Metrics: {} Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan_cifar10_32x32_b25x2_500k_20210728_110906-08b61a44.pth - Config: configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py In Collection: BigGAN Metadata: @@ -26,7 +26,7 @@ Models: - Dataset: Others Metrics: {} Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth - Config: configs/biggan/biggan_ajbrock-sn_8xb32-1500kiters_imagenet1k-128x128.py In Collection: BigGAN Metadata: @@ -36,7 +36,7 @@ Models: - Dataset: Others Metrics: {} Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_is_iter_1328000_20211111_122911-28c688bc.pth - Config: configs/biggan/biggan_cvt-BigGAN-PyTorch-rgb_imagenet1k-128x128.py In Collection: BigGAN Metadata: @@ -48,7 +48,7 @@ Models: FID: 10.1414 IS: 96.728 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_cvt_BigGAN-PyTorch_rgb_20210730_125223-3e353fef.pth - Config: configs/biggan/biggan-deep_cvt-hugging-face-rgb_imagenet1k-128x128.py In Collection: BigGAN Metadata: @@ -60,7 +60,7 @@ Models: FID: 5.9471 IS: 107.161 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_128x128_cvt_hugging-face_rgb_20210728_111659-099e96f9.pth - Config: configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-256x256.py In Collection: BigGAN Metadata: @@ -72,7 +72,7 @@ Models: FID: 11.3151 IS: 135.107 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_256x256_cvt_hugging-face_rgb_20210728_111735-28651569.pth - Config: configs/biggan/biggan-deep_cvt-hugging-face_rgb_imagenet1k-512x512.py In Collection: BigGAN Metadata: @@ -84,4 +84,4 @@ Models: FID: 16.8728 IS: 124.368 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth + Weights: https://download.openmmlab.com/mmediting/biggan/biggan-deep_imagenet1k_512x512_cvt_hugging-face_rgb_20210728_112346-a42585f2.pth diff --git a/configs/cain/README.md b/configs/cain/README.md index 4cfd230bfa..9f2bfdba9b 100644 --- a/configs/cain/README.md +++ b/configs/cain/README.md @@ -24,9 +24,9 @@ Evaluated on RGB channels. The metrics are `PSNR / SSIM` . The learning rate adjustment strategy is `Step LR scheduler with min_lr clipping`. -| Method | PSNR | SSIM | GPU Info | Download | -| :------------------------------------------------------------------------------: | :-----: | :----: | :----------------------: | :---------------------------------------------------------------------------------: | -| [cain_b5_g1b32_vimeo90k_triplet](/configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py) | 34.6010 | 0.9578 | 1 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth)/[log](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.log.json) | +| Method | PSNR | SSIM | GPU Info | Download | +| :---------------------------------------------------------------------: | :-----: | :----: | :----------------------: | :------------------------------------------------------------------------------------------: | +| [cain_b5_g1b32_vimeo90k_triplet](./cain_g1b32_1xb5_vimeo90k-triplet.py) | 34.6010 | 0.9578 | 1 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth)/[log](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.log.json) | ## Quick Start diff --git a/configs/cain/README_zh-CN.md b/configs/cain/README_zh-CN.md index f81da09e57..82860f9781 100644 --- a/configs/cain/README_zh-CN.md +++ b/configs/cain/README_zh-CN.md @@ -27,9 +27,9 @@ 我们使用 `PSNR` 和 `SSIM` 作为指标。 学习率调整策略是等间隔调整策略。 -| 算法 | vimeo-90k-triplet | GPU 信息 | 下载 | -| :-----------------------------------------------------------------------------: | :---------------: | :----------------------: | :------------------------------------------------------------------------------: | -| [cain_b5_g1b32_vimeo90k_triplet](/configs/cain/cain_g1b32_1xb5_vimeo90k-triplet.py) | 34.6010 / 0.9578 | 1 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth)/[日志](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.log.json) | +| 算法 | vimeo-90k-triplet | GPU 信息 | 下载 | +| :---------------------------------------------------------------------: | :---------------: | :----------------------: | :--------------------------------------------------------------------------------------: | +| [cain_b5_g1b32_vimeo90k_triplet](./cain_g1b32_1xb5_vimeo90k-triplet.py) | 34.6010 / 0.9578 | 1 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.pth)/[日志](https://download.openmmlab.com/mmediting/video_interpolators/cain/cain_b5_g1b32_vimeo90k_triplet_20220530-3520b00c.log.json) | ## 快速开始 diff --git a/configs/cyclegan/README.md b/configs/cyclegan/README.md index de27ba78d0..dd408b0d1b 100644 --- a/configs/cyclegan/README.md +++ b/configs/cyclegan/README.md @@ -27,21 +27,21 @@ Image-to-image translation is a class of vision and graphics problems where the We use `FID` and `IS` metrics to evaluate the generation performance of CycleGAN.1 -https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth -https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_in_1x1_80k_facades_20210902_165905-5e2c0876.pth - -| Models | Dataset | FID | IS | Config | Download | -| :----: | :---------------: | :------: | :---: | :-------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------: | -| Ours | facades | 124.8033 | 1.792 | [config](/configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth) \| [log](https://download.openmmlab.com/mmgen/cyclegan/base_cyclegan_in_1x1_80k_facades_20210317_160938.log.json) 2 | -| Ours | facades-id0 | 125.1694 | 1.905 | [config](/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades_convert-bgr_20210902_164411-d8e72b45.pth) | -| Ours | summer2winter | 83.7177 | 2.771 | [config](/configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth) | -| Ours | summer2winter-id0 | 83.1418 | 2.720 | [config](/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth) | -| Ours | winter2summer | 72.8025 | 3.129 | [config](/configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth) | -| Ours | winter2summer-id0 | 73.5001 | 3.107 | [config](/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth) | -| Ours | horse2zebra | 64.5225 | 1.418 | [config](/configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth) | -| Ours | horse2zebra-id0 | 74.7770 | 1.542 | [config](/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth) | -| Ours | zebra2horse | 141.1517 | 3.154 | [config](/configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth) | -| Ours | zebra2horse-id0 | 134.3728 | 3.091 | [config](/configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth) | +https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth +https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_in_1x1_80k_facades_20210902_165905-5e2c0876.pth + +| Models | Dataset | FID | IS | Config | Download | +| :----: | :---------------: | :------: | :---: | :----------------------------------------------------------------------: | :------------------------------------------------------------------------------------------: | +| Ours | facades | 124.8033 | 1.792 | [config](./cyclegan_lsgan-resnet-in_1xb1-80kiters_facades.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth) \| [log](https://download.openmmlab.com/mmediting/cyclegan/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210317_160938.log.json) 2 | +| Ours | facades-id0 | 125.1694 | 1.905 | [config](./cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades_convert-bgr_20210902_164411-d8e72b45.pth) | +| Ours | summer2winter | 83.7177 | 2.771 | [config](./cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth) | +| Ours | summer2winter-id0 | 83.1418 | 2.720 | [config](./cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth) | +| Ours | winter2summer | 72.8025 | 3.129 | [config](./cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth) | +| Ours | winter2summer-id0 | 73.5001 | 3.107 | [config](./cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth) | +| Ours | horse2zebra | 64.5225 | 1.418 | [config](./cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth) | +| Ours | horse2zebra-id0 | 74.7770 | 1.542 | [config](./cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth) | +| Ours | zebra2horse | 141.1517 | 3.154 | [config](./cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth) | +| Ours | zebra2horse-id0 | 134.3728 | 3.091 | [config](./cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py) | [model](https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth) | `FID` comparison with official: diff --git a/configs/cyclegan/metafile.yml b/configs/cyclegan/metafile.yml index 573265939e..29e3eddfd5 100644 --- a/configs/cyclegan/metafile.yml +++ b/configs/cyclegan/metafile.yml @@ -21,7 +21,7 @@ Models: FID: 124.8033 IS: 1.792 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_80k_facades_20210902_165905-5e2c0876.pth - Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-80kiters_facades.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -34,7 +34,7 @@ Models: FID: 125.1694 IS: 1.905 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades_convert-bgr_20210902_164411-d8e72b45.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades_convert-bgr_20210902_164411-d8e72b45.pth - Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -47,7 +47,7 @@ Models: FID: 83.7177 IS: 2.771 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth - Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -60,7 +60,7 @@ Models: FID: 83.1418 IS: 2.72 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth - Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-250kiters_summer2winter.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -73,7 +73,7 @@ Models: FID: 72.8025 IS: 3.129 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165932-fcf08dc1.pth - Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-250kiters_summer2winter.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -86,7 +86,7 @@ Models: FID: 73.5001 IS: 3.107 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_246200_summer2winter_convert-bgr_20210902_165640-8b825581.pth - Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -99,7 +99,7 @@ Models: FID: 64.5225 IS: 1.418 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth - Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -112,7 +112,7 @@ Models: FID: 74.777 IS: 1.542 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth - Config: configs/cyclegan/cyclegan_lsgan-resnet-in_1xb1-270kiters_horse2zebra.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -125,7 +125,7 @@ Models: FID: 141.1517 IS: 3.154 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_170004-a32c733a.pth - Config: configs/cyclegan/cyclegan_lsgan-id0-resnet-in_1xb1-270kiters_horse2zebra.py In Collection: 'CycleGAN: Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks' @@ -138,4 +138,4 @@ Models: FID: 134.3728 IS: 3.091 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth + Weights: https://download.openmmlab.com/mmediting/cyclegan/refactor/cyclegan_lsgan_id0_resnet_in_1x1_266800_horse2zebra_convert-bgr_20210902_165724-77c9c806.pth diff --git a/configs/dcgan/README.md b/configs/dcgan/README.md index 22315a4092..cce01969a8 100644 --- a/configs/dcgan/README.md +++ b/configs/dcgan/README.md @@ -26,11 +26,11 @@ In recent years, supervised learning with convolutional networks (CNNs) has seen -| Models | Dataset | SWD | MS-SSIM | Config | Download | -| :---------: | :------------: | :----------------------: | :-----: | :---------------------------------------------------------------------: | :-----------------------------------------------------------------------: | -| DCGAN 64x64 | MNIST (64x64) | 21.16, 4.4, 8.41/11.32 | 0.1395 | [config](/configs/dcgan/dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py) | [model](https://download.openmmlab.com/mmgen/base_dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth) \| [log](https://download.openmmlab.com//mmgen/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.json) | -| DCGAN 64x64 | CelebA-Cropped | 8.93,10.53,50.32/23.26 | 0.2899 | [config](/configs/dcgan/dcgan_1xb128-300kiters_celeba-cropped-64.py) | [model](https://download.openmmlab.com/mmgen/base_dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth) \| [log](https://download.openmmlab.com/mmgen/dcgan/base_dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.json) | -| DCGAN 64x64 | LSUN-Bedroom | 42.79, 34.55, 98.46/58.6 | 0.2095 | [config](/configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py) | [model](https://download.openmmlab.com/mmgen/base_dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth) \| [log](https://download.openmmlab.com/mmgen/dcgan/base_dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.json) | +| Models | Dataset | SWD | MS-SSIM | Config | Download | +| :---------: | :------------: | :----------------------: | :-----: | :-------------------------------------------------------------: | :-------------------------------------------------------------------------------: | +| DCGAN 64x64 | MNIST (64x64) | 21.16, 4.4, 8.41/11.32 | 0.1395 | [config](./dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py) | [model](https://download.openmmlab.com/mmediting/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth) \| [log](https://download.openmmlab.com//mmgen/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.json) | +| DCGAN 64x64 | CelebA-Cropped | 8.93,10.53,50.32/23.26 | 0.2899 | [config](./dcgan_1xb128-300kiters_celeba-cropped-64.py) | [model](https://download.openmmlab.com/mmediting/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth) \| [log](https://download.openmmlab.com/mmediting/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.json) | +| DCGAN 64x64 | LSUN-Bedroom | 42.79, 34.55, 98.46/58.6 | 0.2095 | [config](./dcgan_1xb128-5epoches_lsun-bedroom-64x64.py) | [model](https://download.openmmlab.com/mmediting/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth) \| [log](https://download.openmmlab.com/mmediting/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.json) | ## Citation diff --git a/configs/dcgan/metafile.yml b/configs/dcgan/metafile.yml index 62d38230c5..5f549b9bce 100644 --- a/configs/dcgan/metafile.yml +++ b/configs/dcgan/metafile.yml @@ -20,7 +20,7 @@ Models: Metrics: MS-SSIM: 0.1395 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/base_dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth + Weights: https://download.openmmlab.com/mmediting/dcgan/dcgan_mnist-64_b128x1_Glr4e-4_Dlr1e-4_5k_20210512_163926-207a1eaf.pth - Config: configs/dcgan/dcgan_1xb128-300kiters_celeba-cropped-64.py In Collection: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks @@ -32,7 +32,7 @@ Models: Metrics: MS-SSIM: 0.2899 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/base_dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth + Weights: https://download.openmmlab.com/mmediting/dcgan/dcgan_celeba-cropped_64_b128x1_300kiter_20210408_161607-1f8a2277.pth - Config: configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py In Collection: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks @@ -44,4 +44,4 @@ Models: Metrics: MS-SSIM: 0.2095 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/base_dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth + Weights: https://download.openmmlab.com/mmediting/dcgan/dcgan_lsun-bedroom_64_b128x1_5e_20210408_161713-117c498b.pth diff --git a/configs/deepfillv1/README.md b/configs/deepfillv1/README.md index d68c28523c..a020f898ea 100644 --- a/configs/deepfillv1/README.md +++ b/configs/deepfillv1/README.md @@ -22,15 +22,15 @@ Recent deep learning based approaches have shown promising results for the chall **Places365-Challenge** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :-------------------------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :---------------------------------------------------------------: | -| [DeepFillv1](/configs/deepfillv1/deepfillv1_8xb2_places-256x256.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :-----------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :-----------------------------------------------------------------------------: | +| [DeepFillv1](./deepfillv1_8xb2_places-256x256.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) | **CelebA-HQ** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :--------------------------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :-----------------------------------------------------------------: | -| [DeepFillv1](/configs/deepfillv1/deepfillv1_4xb4_celeba-256x256.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :-----------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :--------------------------------------------------------------------------------: | +| [DeepFillv1](./deepfillv1_4xb4_celeba-256x256.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) | ## Quick Start diff --git a/configs/deepfillv1/README_zh-CN.md b/configs/deepfillv1/README_zh-CN.md index 273a29ac0a..db7625e8ba 100644 --- a/configs/deepfillv1/README_zh-CN.md +++ b/configs/deepfillv1/README_zh-CN.md @@ -23,15 +23,15 @@ **Places365-Challenge** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :----------------------------------------------------------------: | :---------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :-----------------------------------------------------------------: | -| [DeepFillv1](/configs/deepfillv1/deepfillv1_8xb2_places-256x256.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :-----------------------------------------------: | :---------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :----------------------------------------------------------------------------------: | +| [DeepFillv1](./deepfillv1_8xb2_places-256x256.py) | square bbox | 256x256 | 3500k | Places365-val | 11.019 | 23.429 | 0.862 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_8x2_places_20200619-c00a0e21.log.json) | **CelebA-HQ** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :-----------------------------------------------------------------: | :---------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------: | -| [DeepFillv1](/configs/deepfillv1/deepfillv1_4xb4_celeba-256x256.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :-----------------------------------------------: | :---------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------------------------: | +| [DeepFillv1](./deepfillv1_4xb4_celeba-256x256.py) | square bbox | 256x256 | 1500k | CelebA-val | 6.677 | 26.878 | 0.911 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv1/deepfillv1_256x256_4x4_celeba_20200619-dd51a855.log.json) | ## 快速开始 diff --git a/configs/deepfillv2/README.md b/configs/deepfillv2/README.md index dce7150cb5..096bb2f16c 100644 --- a/configs/deepfillv2/README.md +++ b/configs/deepfillv2/README.md @@ -22,15 +22,15 @@ We present a generative image inpainting system to complete images with free-for **Places365-Challenge** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :--------------------------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :----------------------------------------------------------------: | -| [DeepFillv2](/configs/deepfillv2/deepfillv2_8xb2_places-256x256.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :-----------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :-------------------------------------------------------------------------------: | +| [DeepFillv2](./deepfillv2_8xb2_places-256x256.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) | **CelebA-HQ** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :---------------------------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :------------------------------------------------------------------: | -| [DeepFillv2](/configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :-----------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :----------------------------------------------------------------------------------: | +| [DeepFillv2](./deepfillv2_8xb2_celeba-256x256.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) | ## Quick Start diff --git a/configs/deepfillv2/README_zh-CN.md b/configs/deepfillv2/README_zh-CN.md index 3c71037056..eaa1a31f4a 100644 --- a/configs/deepfillv2/README_zh-CN.md +++ b/configs/deepfillv2/README_zh-CN.md @@ -23,15 +23,15 @@ **Places365-Challenge** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :-----------------------------------------------------------------: | :-------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :------------------------------------------------------------------: | -| [DeepFillv2](/configs/deepfillv2/deepfillv2_8xb2_places-256x256.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :-----------------------------------------------: | :-------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :------------------------------------------------------------------------------------: | +| [DeepFillv2](./deepfillv2_8xb2_places-256x256.py) | free-form | 256x256 | 100k | Places365-val | 8.635 | 22.398 | 0.815 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_places_20200619-10d15793.log.json) | **CelebA-HQ** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :-----------------------------------------------------------------: | :-------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :---------------------------------------------------------------------: | -| [DeepFillv2](/configs/deepfillv2/deepfillv2_8xb2_celeba-256x256.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :-----------------------------------------------: | :-------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :---------------------------------------------------------------------------------------: | +| [DeepFillv2](./deepfillv2_8xb2_celeba-256x256.py) | free-form | 256x256 | 20k | CelebA-val | 5.411 | 25.721 | 0.871 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/deepfillv2/deepfillv2_256x256_8x2_celeba_20200619-c96e5f12.log.json) | ## 快速开始 diff --git a/configs/dic/README.md b/configs/dic/README.md index 69b808f74b..90215ae6b8 100644 --- a/configs/dic/README.md +++ b/configs/dic/README.md @@ -27,10 +27,10 @@ In the log data of `dic_gan_x8c48b6_g4_150k_CelebAHQ`, DICGAN is verified on the `GPU Info`: GPU information during training. -| Method | scale | PSNR | SSIM | GPU Info | Download | -| :------------------------------------------------------------------------------: | :---: | :-----: | :----: | :-----------------: | :---------------------------------------------------------------------------------: | -| [dic_x8c48b6_g4_150k_CelebAHQ](/configs/dic/dic_x8c48b6_4xb2-150k_celeba-hq.py) | x8 | 25.2319 | 0.7422 | 4 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.log.json) | -| [dic_gan_x8c48b6_g4_500k_CelebAHQ](/configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py) | x8 | 23.6241 | 0.6721 | 4 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.log.json) | +| Method | scale | PSNR | SSIM | GPU Info | Download | +| :--------------------------------------------------------------------------: | :---: | :-----: | :----: | :-----------------: | :-------------------------------------------------------------------------------------: | +| [dic_x8c48b6_g4_150k_CelebAHQ](./dic_x8c48b6_4xb2-150k_celeba-hq.py) | x8 | 25.2319 | 0.7422 | 4 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.log.json) | +| [dic_gan_x8c48b6_g4_500k_CelebAHQ](./dic_gan-x8c48b6_4xb2-500k_celeba-hq.py) | x8 | 23.6241 | 0.6721 | 4 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.log.json) | ## Quick Start diff --git a/configs/dic/README_zh-CN.md b/configs/dic/README_zh-CN.md index 8333162f54..74ef56d31b 100644 --- a/configs/dic/README_zh-CN.md +++ b/configs/dic/README_zh-CN.md @@ -29,10 +29,10 @@ `GPU 信息`: 训练过程中的 GPU 信息. -| 算法 | scale | CelebA-HQ | GPU 信息 | 下载 | -| :------------------------------------------------------------------------------: | :---: | :--------------: | :-----------------: | :------------------------------------------------------------------------------: | -| [dic_x8c48b6_g4_150k_CelebAHQ](/configs/dic/dic_x8c48b6_4xb2-150k_celeba-hq.py) | x8 | 25.2319 / 0.7422 | 4 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.log.json) | -| [dic_gan_x8c48b6_g4_500k_CelebAHQ](/configs/dic/dic_gan-x8c48b6_4xb2-500k_celeba-hq.py) | x8 | 23.6241 / 0.6721 | 4 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.log.json) | +| 算法 | scale | CelebA-HQ | GPU 信息 | 下载 | +| :--------------------------------------------------------------------------: | :---: | :--------------: | :-----------------: | :----------------------------------------------------------------------------------: | +| [dic_x8c48b6_g4_150k_CelebAHQ](./dic_x8c48b6_4xb2-150k_celeba-hq.py) | x8 | 25.2319 / 0.7422 | 4 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/dic/dic_x8c48b6_g4_150k_CelebAHQ_20210611-5d3439ca.log.json) | +| [dic_gan_x8c48b6_g4_500k_CelebAHQ](./dic_gan-x8c48b6_4xb2-500k_celeba-hq.py) | x8 | 23.6241 / 0.6721 | 4 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/dic/dic_gan_x8c48b6_g4_500k_CelebAHQ_20210625-3b89a358.log.json) | ## 快速开始 diff --git a/configs/dim/README.md b/configs/dim/README.md index f2c768d070..283ceef496 100644 --- a/configs/dim/README.md +++ b/configs/dim/README.md @@ -20,13 +20,13 @@ Image matting is a fundamental computer vision problem and has many applications ## Results and models -| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | -| :------------------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :-----------------------------------------------------------------------------------------: | -| stage1 (paper) | 54.6 | 0.017 | 36.7 | 55.3 | - | - | -| stage3 (paper) | **50.4** | **0.014** | 31.0 | 50.8 | - | - | -| [stage1 (our)](/configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py) | 53.8 | 0.017 | 32.7 | 54.5 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_20200605_140257.log.json) | -| [stage2 (our)](/configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py) | 52.3 | 0.016 | 29.4 | 52.4 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_20200607_171909.log.json) | -| [stage3 (our)](/configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py) | 50.6 | 0.015 | **29.0** | **50.7** | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_20200609_111851.log.json) | +| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | +| :-------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :----------------------------------------------------------------------------------------------------: | +| stage1 (paper) | 54.6 | 0.017 | 36.7 | 55.3 | - | - | +| stage3 (paper) | **50.4** | **0.014** | 31.0 | 50.8 | - | - | +| [stage1 (our)](./dim_stage1-v16_1xb1-1000k_comp1k.py) | 53.8 | 0.017 | 32.7 | 54.5 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_20200605_140257.log.json) | +| [stage2 (our)](./dim_stage2-v16-pln_1xb1-1000k_comp1k.py) | 52.3 | 0.016 | 29.4 | 52.4 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_20200607_171909.log.json) | +| [stage3 (our)](./dim_stage3-v16-pln_1xb1-1000k_comp1k.py) | 50.6 | 0.015 | **29.0** | **50.7** | 1 | [model](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_20200609_111851.log.json) | **NOTE** diff --git a/configs/dim/README_zh-CN.md b/configs/dim/README_zh-CN.md index 4683b3efc0..681b592b66 100644 --- a/configs/dim/README_zh-CN.md +++ b/configs/dim/README_zh-CN.md @@ -21,13 +21,13 @@
-| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | -| :---------------------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :--------------------------------------------------------------------------------------: | -| 第一阶段 (原文) | 54.6 | 0.017 | 36.7 | 55.3 | - | - | -| 第三阶段 (原文) | **50.4** | **0.014** | 31.0 | 50.8 | - | - | -| [第一阶段 (复现)](/configs/dim/dim_stage1-v16_1xb1-1000k_comp1k.py) | 53.8 | 0.017 | 32.7 | 54.5 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_20200605_140257.log.json) | -| [第二阶段 (复现)](/configs/dim/dim_stage2-v16-pln_1xb1-1000k_comp1k.py) | 52.3 | 0.016 | 29.4 | 52.4 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_20200607_171909.log.json) | -| [第三阶段 (复现)](/configs/dim/dim_stage3-v16-pln_1xb1-1000k_comp1k.py) | 50.6 | 0.015 | **29.0** | **50.7** | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_20200609_111851.log.json) | +| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | +| :----------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :-------------------------------------------------------------------------------------------------: | +| 第一阶段 (原文) | 54.6 | 0.017 | 36.7 | 55.3 | - | - | +| 第三阶段 (原文) | **50.4** | **0.014** | 31.0 | 50.8 | - | - | +| [第一阶段 (复现)](./dim_stage1-v16_1xb1-1000k_comp1k.py) | 53.8 | 0.017 | 32.7 | 54.5 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_SAD-53.8_20200605_140257-979a420f.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage1_v16_1x1_1000k_comp1k_20200605_140257.log.json) | +| [第二阶段 (复现)](./dim_stage2-v16-pln_1xb1-1000k_comp1k.py) | 52.3 | 0.016 | 29.4 | 52.4 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_SAD-52.3_20200607_171909-d83c4775.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage2_v16_pln_1x1_1000k_comp1k_20200607_171909.log.json) | +| [第三阶段 (复现)](./dim_stage3-v16-pln_1xb1-1000k_comp1k.py) | 50.6 | 0.015 | **29.0** | **50.7** | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_SAD-50.6_20200609_111851-647f24b6.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/dim/dim_stage3_v16_pln_1x1_1000k_comp1k_20200609_111851.log.json) | **注** diff --git a/configs/edsr/README.md b/configs/edsr/README.md index b375f49f0d..c49922a093 100644 --- a/configs/edsr/README.md +++ b/configs/edsr/README.md @@ -25,9 +25,9 @@ The metrics are `PSNR / SSIM` . | Method | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU Info | Download | | :----------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :------: | :------------------------------------------------------------------: | -| [edsr_x2c64b16_1x16_300k_div2k](/configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py) | 35.7592 | 31.4290 | 34.5896 | 0.9372 | 0.8874 | 0.9352 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) | -| [edsr_x3c64b16_1x16_300k_div2k](/configs/edsr/edsr_x3c64b16_1xb16-300k_div2k.py) | 32.3301 | 28.4125 | 30.9154 | 0.8912 | 0.8022 | 0.8711 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) | -| [edsr_x4c64b16_1x16_300k_div2k](/configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py) | 30.2223 | 26.7870 | 28.9675 | 0.8500 | 0.7366 | 0.8172 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) | +| [edsr_x2c64b16_1x16_300k_div2k](./edsr_x2c64b16_1xb16-300k_div2k.py) | 35.7592 | 31.4290 | 34.5896 | 0.9372 | 0.8874 | 0.9352 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) | +| [edsr_x3c64b16_1x16_300k_div2k](./edsr_x3c64b16_1xb16-300k_div2k.py) | 32.3301 | 28.4125 | 30.9154 | 0.8912 | 0.8022 | 0.8711 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) | +| [edsr_x4c64b16_1x16_300k_div2k](./edsr_x4c64b16_1xb16-300k_div2k.py) | 30.2223 | 26.7870 | 28.9675 | 0.8500 | 0.7366 | 0.8172 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) | ## Quick Start diff --git a/configs/edsr/README_zh-CN.md b/configs/edsr/README_zh-CN.md index 59579d6728..1668c56959 100644 --- a/configs/edsr/README_zh-CN.md +++ b/configs/edsr/README_zh-CN.md @@ -24,11 +24,11 @@ 在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。 我们使用 `PSNR` 和 `SSIM` 作为指标。 -| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | -| :----------------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :------: | :----------------------------------------------------------------------: | -| [edsr_x2c64b16_1x16_300k_div2k](/configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py) | 35.7592 / 0.9372 | 31.4290 / 0.8874 | 34.5896 / 0.9352 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) | -| [edsr_x3c64b16_1x16_300k_div2k](/configs/edsr/edsr_x3c64b16_1xb16-300k_div2k.py) | 32.3301 / 0.8912 | 28.4125 / 0.8022 | 30.9154 / 0.8711 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) | -| [edsr_x4c64b16_1x16_300k_div2k](/configs/edsr/edsr_x4c64b16_1xb16-300k_div2k.py) | 30.2223 / 0.8500 | 26.7870 / 0.7366 | 28.9675 / 0.8172 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) | +| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | +| :------------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :------: | :--------------------------------------------------------------------------: | +| [edsr_x2c64b16_1x16_300k_div2k](./edsr_x2c64b16_1xb16-300k_div2k.py) | 35.7592 / 0.9372 | 31.4290 / 0.8874 | 34.5896 / 0.9352 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604-19fe95ea.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x2c64b16_1x16_300k_div2k_20200604_221933.log.json) | +| [edsr_x3c64b16_1x16_300k_div2k](./edsr_x3c64b16_1xb16-300k_div2k.py) | 32.3301 / 0.8912 | 28.4125 / 0.8022 | 30.9154 / 0.8711 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608-36d896f4.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x3c64b16_1x16_300k_div2k_20200608_114850.log.json) | +| [edsr_x4c64b16_1x16_300k_div2k](./edsr_x4c64b16_1xb16-300k_div2k.py) | 30.2223 / 0.8500 | 26.7870 / 0.7366 | 28.9675 / 0.8172 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608-3c2af8a3.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edsr/edsr_x4c64b16_1x16_300k_div2k_20200608_115148.log.json) | ## 快速开始 diff --git a/configs/edvr/README.md b/configs/edvr/README.md index d0c74c9299..e5de63a3de 100644 --- a/configs/edvr/README.md +++ b/configs/edvr/README.md @@ -25,17 +25,17 @@ The metrics are `PSNR and SSIM` . | Method | PSNR | GPU Info | Download | | :---------------------------------------------------------------------------------: | :-----: | :----------------------: | :------------------------------------------------------------------------------------: | -| [edvrm_wotsa_x4_8x4_600k_reds](/configs/edvr/edvrm_wotsa_8xb4-600k_reds.py) | 30.3430 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) | -| [edvrm_x4_8x4_600k_reds](/configs/edvr/edvrm_8xb4-600k_reds.py) | 30.4194 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) | -| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](/configs/edvr/edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0010 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) | -| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](/configs/edvr/edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0467 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) | +| [edvrm_wotsa_x4_8x4_600k_reds](./edvrm_wotsa_8xb4-600k_reds.py) | 30.3430 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) | +| [edvrm_x4_8x4_600k_reds](./edvrm_8xb4-600k_reds.py) | 30.4194 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) | +| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0010 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) | +| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0467 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) | | Method | SSIM | GPU Info | Download | | :----------------------------------------------------------------------------------: | :----: | :----------------------: | :------------------------------------------------------------------------------------: | -| [edvrm_wotsa_x4_8x4_600k_reds](/configs/edvr/edvrm_wotsa_8xb4-600k_reds.py) | 0.8664 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) | -| [edvrm_x4_8x4_600k_reds](/configs/edvr/edvrm_8xb4-600k_reds.py) | 0.8684 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) | -| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](/configs/edvr/edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 0.8784 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) | -| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](/configs/edvr/edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 0.8793 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) | +| [edvrm_wotsa_x4_8x4_600k_reds](./edvrm_wotsa_8xb4-600k_reds.py) | 0.8664 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) | +| [edvrm_x4_8x4_600k_reds](./edvrm_8xb4-600k_reds.py) | 0.8684 | 8 | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) | +| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 0.8784 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) | +| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 0.8793 | 8 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) | ## Quick Start diff --git a/configs/edvr/README_zh-CN.md b/configs/edvr/README_zh-CN.md index d5bdfe52a2..d8d14809d2 100644 --- a/configs/edvr/README_zh-CN.md +++ b/configs/edvr/README_zh-CN.md @@ -26,10 +26,10 @@ | 算法 | REDS4 | GPU 信息 | 下载 | | :------------------------------------------------------------------------------: | :--------------: | :----------------------: | :------------------------------------------------------------------------------: | -| [edvrm_wotsa_x4_8x4_600k_reds](/configs/edvr/edvrm_wotsa_8xb4-600k_reds.py) | 30.3430 / 0.8664 | 8 | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) | -| [edvrm_x4_8x4_600k_reds](/configs/edvr/edvrm_8xb4-600k_reds.py) | 30.4194 / 0.8684 | 8 | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) | -| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](/configs/edvr/edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0010 / 0.8784 | 8 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) | -| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](/configs/edvr/edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0467 / 0.8793 | 8 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) | +| [edvrm_wotsa_x4_8x4_600k_reds](./edvrm_wotsa_8xb4-600k_reds.py) | 30.3430 / 0.8664 | 8 | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522-0570e567.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_wotsa_x4_8x4_600k_reds_20200522_141644.log.json) | +| [edvrm_x4_8x4_600k_reds](./edvrm_8xb4-600k_reds.py) | 30.4194 / 0.8684 | 8 | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20210625-e29b71b5.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrm_x4_8x4_600k_reds_20200622_102544.log.json) | +| [edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_wotsa-c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0010 / 0.8784 | 8 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228-d895a769.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_wotsa_c128b40_8x8_lr2e-4_600k_reds4_20211228_144658.log.json) | +| [edvrl_c128b40_8x8_lr2e-4_600k_reds4](./edvrl_c128b40_8xb8-lr2e-4-600k_reds4.py) | 31.0467 / 0.8793 | 8 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104-4509865f.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/edvr/edvrl_c128b40_8x8_lr2e-4_600k_reds4_20220104_171823.log.json) | ## 快速开始 diff --git a/configs/eg3d/README.md b/configs/eg3d/README.md index a1de6c75f8..7ef79dcf32 100644 --- a/configs/eg3d/README.md +++ b/configs/eg3d/README.md @@ -20,11 +20,11 @@ Unsupervised generation of high-quality multi-view-consistent images and 3D shap ## Results and Models -| Model | Comment | FID50k | FID50k-Camera | Config | Download | -| :----------: | :-------------: | :----: | :-----------: | :---------------------------------------------------------------: | :---------------------------------------------------------------------------------------: | -| ShapeNet-Car | official weight | 5.6573 | 5.2325 | [config](/configs/eg3d/eg3d_cvt-official-rgb_shapenet-128x128.py) | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_shapenet-128x128-85757f4d.pth) | -| AFHQ | official weight | 2.9134 | 6.4213 | [config](/configs/eg3d/eg3d_cvt-official-rgb_afhq-512x512.py) | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_afhq-512x512-ca1dd7c9.pth) | -| FFHQ | official weight | 4.3076 | 6.4453 | [config](configs/eg3d/eg3d_cvt-official-rgb_ffhq-512x512.py) | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_ffhq-512x512-5a0ddcb6.pth) | +| Model | Comment | FID50k | FID50k-Camera | Config | Download | +| :----------: | :-------------: | :----: | :-----------: | :---------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | +| ShapeNet-Car | official weight | 5.6573 | 5.2325 | [config](./eg3d_cvt-official-rgb_shapenet-128x128.py) | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_shapenet-128x128-85757f4d.pth) | +| AFHQ | official weight | 2.9134 | 6.4213 | [config](./eg3d_cvt-official-rgb_afhq-512x512.py) | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_afhq-512x512-ca1dd7c9.pth) | +| FFHQ | official weight | 4.3076 | 6.4453 | [config](./eg3d_cvt-official-rgb_ffhq-512x512.py) | [model](https://download.openmmlab.com/mmediting/eg3d/eg3d_cvt-official-rgb_ffhq-512x512-5a0ddcb6.pth) | - `FID50k-Camera` denotes image generated with random sampled camera position. - `FID50k` denotes image generated with camera position randomly sampled from the original dataset. @@ -32,7 +32,7 @@ Unsupervised generation of high-quality multi-view-consistent images and 3D shap ### Influence of FP16 All metrics are evaluated under FP32, and it's hard to determine how they will change if we use FP16. -For example, if we use FP16 at the super resolution module in [FFHQ model](/configs/eg3d_ffhq.py), the output images will be slightly blurrier than the ones generated under FP32, but FID (**4.03**) will be better than FP32 ones. +For example, if we use FP16 at the super resolution module in [FFHQ model](./eg3d_cvt-official-rgb_ffhq-512x512.py), the output images will be slightly blurrier than the ones generated under FP32, but FID (**4.03**) will be better than FP32 ones. ## About generate images and videos with High-Level API diff --git a/configs/esrgan/README.md b/configs/esrgan/README.md index dd5f84b783..95af8fead6 100644 --- a/configs/esrgan/README.md +++ b/configs/esrgan/README.md @@ -25,8 +25,8 @@ The metrics are `PSNR / SSIM` . | Method | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU Info | Download | | :----------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :------: | :------------------------------------------------------------------: | -| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](/configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | 30.6428 | 27.0543 | 29.3354 | 0.8559 | 0.7447 | 0.8263 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420_112550.log.json) | -| [esrgan_x4c64b23g32_1x16_400k_div2k](/configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py) | 28.2700 | 24.6328 | 26.6531 | 0.7778 | 0.6491 | 0.7340 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508_191042.log.json) | +| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](./esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | 30.6428 | 27.0543 | 29.3354 | 0.8559 | 0.7447 | 0.8263 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420_112550.log.json) | +| [esrgan_x4c64b23g32_1x16_400k_div2k](./esrgan_x4c64b23g32_1xb16-400k_div2k.py) | 28.2700 | 24.6328 | 26.6531 | 0.7778 | 0.6491 | 0.7340 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508_191042.log.json) | ## Quick Start diff --git a/configs/esrgan/README_zh-CN.md b/configs/esrgan/README_zh-CN.md index 48ba0ac400..c569397179 100644 --- a/configs/esrgan/README_zh-CN.md +++ b/configs/esrgan/README_zh-CN.md @@ -26,8 +26,8 @@ | 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | | :---------------------------------------------------------------------: | :---------------: | :--------------: | :--------------: | :------: | :----------------------------------------------------------------------: | -| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](/configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | 30.6428 / 0.8559 | 27.0543 / 0.7447 | 29.3354 / 0.8263 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420_112550.log.json) | -| [esrgan_x4c64b23g32_1x16_400k_div2k](/configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py) | 28.2700 / 0.7778 | 24.6328 / 0.6491 | 26.6531 / 0.7340 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508_191042.log.json) | +| [esrgan_psnr_x4c64b23g32_1x16_1000k_div2k](./esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py) | 30.6428 / 0.8559 | 27.0543 / 0.7447 | 29.3354 / 0.8263 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420-bf5c993c.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_psnr_x4c64b23g32_1x16_1000k_div2k_20200420_112550.log.json) | +| [esrgan_x4c64b23g32_1x16_400k_div2k](./esrgan_x4c64b23g32_1xb16-400k_div2k.py) | 28.2700 / 0.7778 | 24.6328 / 0.6491 | 26.6531 / 0.7340 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508-f8ccaf3b.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/esrgan/esrgan_x4c64b23g32_1x16_400k_div2k_20200508_191042.log.json) | ## 快速开始 diff --git a/configs/flavr/README.md b/configs/flavr/README.md index b9e1a8c817..453137d8b1 100644 --- a/configs/flavr/README.md +++ b/configs/flavr/README.md @@ -25,7 +25,7 @@ The metrics are `PSNR / SSIM` . | Method | scale | PSNR | SSIM | GPU Info | Download | | :------------------------------------------------------------------------------: | :---: | :-----: | :-----: | :-----------------: | :--------------------------------------------------------------------------------: | -| [flavr_in4out1_g8b4_vimeo90k_septuplet](/configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py) | x2 | 36.3340 | 0.96015 | 8 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.log.json) | +| [flavr_in4out1_g8b4_vimeo90k_septuplet](./flavr_in4out1_8xb4_vimeo90k-septuplet.py) | x2 | 36.3340 | 0.96015 | 8 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.log.json) | Note: FLAVR for x8 VFI task will supported in the future. diff --git a/configs/flavr/README_zh-CN.md b/configs/flavr/README_zh-CN.md index 31ff68a917..1ec3783c28 100644 --- a/configs/flavr/README_zh-CN.md +++ b/configs/flavr/README_zh-CN.md @@ -13,7 +13,7 @@ | 算法 | scale | Vimeo90k-triplet | GPU 信息 | 下载 | | :-----------------------------------------------------------------------------: | :---: | :---------------: | :-----------------: | :------------------------------------------------------------------------------: | -| [flavr_in4out1_g8b4_vimeo90k_septuplet](/configs/flavr/flavr_in4out1_8xb4_vimeo90k-septuplet.py) | x2 | 36.3340 / 0.96015 | 8 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.log.json) | +| [flavr_in4out1_g8b4_vimeo90k_septuplet](./flavr_in4out1_8xb4_vimeo90k-septuplet.py) | x2 | 36.3340 / 0.96015 | 8 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/flavr/flavr_in4out1_g8b4_vimeo90k_septuplet_20220509-c2468995.log.json) | 注：FLAVR 中的 8 倍视频插帧算法将会在未来版本中支持。 diff --git a/configs/gca/README.md b/configs/gca/README.md index 7c8a9b8913..8abbe00c06 100644 --- a/configs/gca/README.md +++ b/configs/gca/README.md @@ -20,19 +20,19 @@ Over the last few years, deep learning based approaches have achieved outstandin ## Results and models -| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | -| :--------------------------------------------------------------: | :-------: | :--------: | :-------: | :-------: | :------: | :-----------------------------------------------------------------------------------------: | -| baseline (paper) | 40.62 | 0.0106 | 21.53 | 38.43 | - | - | -| GCA (paper) | 35.28 | 0.0091 | 16.92 | 32.53 | - | - | -| [baseline (our)](/configs/gca/baseline_r34_4xb10-200k_comp1k.py) | 34.61 | 0.0083 | 16.21 | 32.12 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.log) | -| [GCA (our)](/configs/gca/gca_r34_4xb10-200k_comp1k.py) | **33.38** | **0.0081** | **14.96** | **30.59** | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.log) | +| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | +| :---------------------------------------------------: | :-------: | :--------: | :-------: | :-------: | :------: | :----------------------------------------------------------------------------------------------------: | +| baseline (paper) | 40.62 | 0.0106 | 21.53 | 38.43 | - | - | +| GCA (paper) | 35.28 | 0.0091 | 16.92 | 32.53 | - | - | +| [baseline (our)](./baseline_r34_4xb10-200k_comp1k.py) | 34.61 | 0.0083 | 16.21 | 32.12 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.log) | +| [GCA (our)](./gca_r34_4xb10-200k_comp1k.py) | **33.38** | **0.0081** | **14.96** | **30.59** | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.log) | **More results** -| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | -| :----------------------------------------------------------------------------------: | :---: | :----: | :---: | :---: | :------: | :-------------------------------------------------------------------------------------: | -| [baseline (with DIM pipeline)](/configs/gca/baseline_r34_4xb10-dimaug-200k_comp1k.py) | 49.95 | 0.0144 | 30.21 | 49.67 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_20200626_231612.log.json) | -| [GCA (with DIM pipeline)](/configs/gca/gca_r34_4xb10-dimaug-200k_comp1k.py) | 49.42 | 0.0129 | 28.07 | 49.47 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_20200626_231422.log.json) | +| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | +| :------------------------------------------------------------------------: | :---: | :----: | :---: | :---: | :------: | :-----------------------------------------------------------------------------------------------: | +| [baseline (with DIM pipeline)](./baseline_r34_4xb10-dimaug-200k_comp1k.py) | 49.95 | 0.0144 | 30.21 | 49.67 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_20200626_231612.log.json) | +| [GCA (with DIM pipeline)](./gca_r34_4xb10-dimaug-200k_comp1k.py) | 49.42 | 0.0129 | 28.07 | 49.47 | 4 | [model](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_20200626_231422.log.json) | ## Quick Start diff --git a/configs/gca/README_zh-CN.md b/configs/gca/README_zh-CN.md index ac7ad9e407..c995d8141b 100644 --- a/configs/gca/README_zh-CN.md +++ b/configs/gca/README_zh-CN.md @@ -20,19 +20,19 @@
-| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | -| :-----------------------------------------------------------: | :-------: | :--------: | :-------: | :-------: | :------: | :--------------------------------------------------------------------------------------------: | -| 基线 (原文) | 40.62 | 0.0106 | 21.53 | 38.43 | - | - | -| GCA (原文) | 35.28 | 0.0091 | 16.92 | 32.53 | - | - | -| [基线 (复现)](/configs/gca/baseline_r34_4xb10-200k_comp1k.py) | 34.61 | 0.0083 | 16.21 | 32.12 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.log) | -| [GCA (复现)](/configs/gca/gca_r34_4xb10-200k_comp1k.py) | **33.38** | **0.0081** | **14.96** | **30.59** | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.log) | +| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | +| :------------------------------------------------: | :-------: | :--------: | :-------: | :-------: | :------: | :-------------------------------------------------------------------------------------------------------: | +| 基线 (原文) | 40.62 | 0.0106 | 21.53 | 38.43 | - | - | +| GCA (原文) | 35.28 | 0.0091 | 16.92 | 32.53 | - | - | +| [基线 (复现)](./baseline_r34_4xb10-200k_comp1k.py) | 34.61 | 0.0083 | 16.21 | 32.12 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/baseline_r34_4x10_200k_comp1k_SAD-34.61_20220620-96f85d56.log) | +| [GCA (复现)](./gca_r34_4xb10-200k_comp1k.py) | **33.38** | **0.0081** | **14.96** | **30.59** | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/gca_r34_4x10_200k_comp1k_SAD-33.38_20220615-65595f39.log) | **其他结果** -| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | -| :-----------------------------------------------------------------------------: | :---: | :----: | :---: | :---: | :------: | :------------------------------------------------------------------------------------------: | -| [基线 (使用 DIM 流水线)](/configs/gca/baseline_r34_4xb10-dimaug-200k_comp1k.py) | 49.95 | 0.0144 | 30.21 | 49.67 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_20200626_231612.log.json) | -| [GCA (使用 DIM 流水线)](/configs/gca/gca_r34_4xb10-dimaug-200k_comp1k.py) | 49.42 | 0.0129 | 28.07 | 49.47 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_20200626_231422.log.json) | +| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | +| :------------------------------------------------------------------: | :---: | :----: | :---: | :---: | :------: | :-----------------------------------------------------------------------------------------------------: | +| [基线 (使用 DIM 流水线)](./baseline_r34_4xb10-dimaug-200k_comp1k.py) | 49.95 | 0.0144 | 30.21 | 49.67 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_SAD-49.95_20200626_231612-535c9a11.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/baseline_dimaug_r34_4x10_200k_comp1k_20200626_231612.log.json) | +| [GCA (使用 DIM 流水线)](./gca_r34_4xb10-dimaug-200k_comp1k.py) | 49.42 | 0.0129 | 28.07 | 49.47 | 4 | [模型](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_SAD-49.42_20200626_231422-8e9cc127.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/gca/gca_dimaug_r34_4x10_200k_comp1k_20200626_231422.log.json) | ## 快速开始 diff --git a/configs/ggan/README.md b/configs/ggan/README.md index d282f0c258..f09f6062ab 100644 --- a/configs/ggan/README.md +++ b/configs/ggan/README.md @@ -28,9 +28,9 @@ Generative Adversarial Nets (GANs) represent an important milestone for effectiv | Models | Dataset | SWD | MS-SSIM | FID | Config | Download | | :----------: | :------------: | :-----------------------------: | :-----: | :-----: | :-------------------------------------------------------------: | :----------------------------------------------------------------: | -| GGAN 64x64 | CelebA-Cropped | 11.18, 12.21, 39.16/20.85 | 0.3318 | 20.1797 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/ggan/ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py) | [model](https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth) \| [log](https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210430_113839.log.json) | -| GGAN 128x128 | CelebA-Cropped | 9.81, 11.29, 19.22, 47.79/22.03 | 0.3149 | 18.7647 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/ggan/ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth) \| [log](https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210423_154258.log.json) | -| GGAN 64x64 | LSUN-Bedroom | 9.1, 6.2, 12.27/9.19 | 0.0649 | 39.9261 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/ggan/ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py) | [model](https://download.openmmlab.com/mmgen/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth) \| [log](https://download.openmmlab.com/mmgen/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210428_202027.log.json) | +| GGAN 64x64 | CelebA-Cropped | 11.18, 12.21, 39.16/20.85 | 0.3318 | 20.1797 | [config](./ggan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py) | [model](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth) \| [log](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210430_113839.log.json) | +| GGAN 128x128 | CelebA-Cropped | 9.81, 11.29, 19.22, 47.79/22.03 | 0.3149 | 18.7647 | [config](./ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth) \| [log](https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210423_154258.log.json) | +| GGAN 64x64 | LSUN-Bedroom | 9.1, 6.2, 12.27/9.19 | 0.0649 | 39.9261 | [config](./ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py) | [model](https://download.openmmlab.com/mmediting/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth) \| [log](https://download.openmmlab.com/mmediting/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210428_202027.log.json) | Note: In the original implementation of [GGAN](https://github.com/lim0606/pytorch-geometric-gan), they set `G_iters` to 10. However our framework does not support `G_iters` currently, so we dropped the settings in the original implementation and conducted several experiments with our own settings. We have shown above the experiment results with the lowest `fid` score. \ Original settings and our settings: diff --git a/configs/ggan/metafile.yml b/configs/ggan/metafile.yml index ace7d6e4eb..e545e21b65 100644 --- a/configs/ggan/metafile.yml +++ b/configs/ggan/metafile.yml @@ -18,7 +18,7 @@ Models: FID: 20.1797 MS-SSIM: 0.3318 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth + Weights: https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m.pth - Config: configs/ggan/ggan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py In Collection: GGAN Metadata: @@ -30,7 +30,7 @@ Models: FID: 18.7647 MS-SSIM: 0.3149 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth + Weights: https://download.openmmlab.com/mmediting/ggan/ggan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210430_143027-516423dc.pth - Config: configs/ggan/ggan_lsgan-archi_lr1e-4-1xb128-20Mimgs_lsun-bedroom-64x64.py In Collection: GGAN Metadata: @@ -42,4 +42,4 @@ Models: FID: 39.9261 MS-SSIM: 0.0649 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth + Weights: https://download.openmmlab.com/mmediting/ggan/ggan_lsun-bedroom_lsgan_archi_lr-1e-4_64_b128x1_20m_20210430_143114-5d99b76c.pth diff --git a/configs/glean/README.md b/configs/glean/README.md index 6d881993d0..3da41f7b98 100644 --- a/configs/glean/README.md +++ b/configs/glean/README.md @@ -24,13 +24,13 @@ For the meta info used in training and test, please refer to [here](https://gith | Method | PSNR | GPU Info | Download | | :----------------------------------------------------------------------------------: | :---: | :----------------------: | :-------------------------------------------------------------------------------------: | -| [glean_cat_8x](/configs/glean/glean_x8_2xb8_cat.py) | 23.98 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) | -| [glean_ffhq_16x](/configs/glean/glean_x16_2xb8_ffhq.py) | 26.91 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) | -| [glean_cat_16x](/configs/glean/glean_x16_2xb8_cat.py) | 20.88 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) | -| [glean_in128out1024_4x2_300k_ffhq_celebahq](/configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py) | 27.94 | 4 (Tesla V100-SXM3-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) | -| [glean_fp16_cat_8x](/configs/glean/glean_x8-fp16_2xb8_cat.py) | - | - | - | -| [glean_fp16_ffhq_16x](/configs/glean/glean_x16-fp16_2xb8_ffhq.py) | - | - | - | -| [glean_fp16_in128out1024_4x2_300k_ffhq_celebahq](/configs/glean/glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py) | - | - | - | +| [glean_cat_8x](./glean_x8_2xb8_cat.py) | 23.98 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) | +| [glean_ffhq_16x](./glean_x16_2xb8_ffhq.py) | 26.91 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) | +| [glean_cat_16x](./glean_x16_2xb8_cat.py) | 20.88 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) | +| [glean_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py) | 27.94 | 4 (Tesla V100-SXM3-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) | +| [glean_fp16_cat_8x](./glean_x8-fp16_2xb8_cat.py) | - | - | - | +| [glean_fp16_ffhq_16x](./glean_x16-fp16_2xb8_ffhq.py) | - | - | - | +| [glean_fp16_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py) | - | - | - | ## Quick Start diff --git a/configs/glean/README_zh-CN.md b/configs/glean/README_zh-CN.md index 2d47a7c713..3bd66eb8ce 100644 --- a/configs/glean/README_zh-CN.md +++ b/configs/glean/README_zh-CN.md @@ -24,13 +24,13 @@ | 算法 | PSNR | GPU 信息 | 下载 | | :-----------------------------------------------------------------------------------: | :---: | :----------------------: | :------------------------------------------------------------------------------------: | -| [glean_cat_8x](/configs/glean/glean_x8_2xb8_cat.py) | 23.98 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) | -| [glean_ffhq_16x](/configs/glean/glean_x16_2xb8_ffhq.py) | 26.91 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) | -| [glean_cat_16x](/configs/glean/glean_x16_2xb8_cat.py) | 20.88 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) | -| [glean_in128out1024_4x2_300k_ffhq_celebahq](/configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py) | 27.94 | 4 (Tesla V100-SXM3-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) | -| [glean_fp16_cat_8x](/configs/glean/glean_x8-fp16_2xb8_cat.py) | - | - | - | -| [glean_fp16_ffhq_16x](/configs/glean/glean_x16-fp16_2xb8_ffhq.py) | - | - | - | -| [glean_fp16_in128out1024_4x2_300k_ffhq_celebahq](/configs/glean/glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py) | - | - | - | +| [glean_cat_8x](./glean_x8_2xb8_cat.py) | 23.98 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614-d3ac8683.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_8x_20210614_145540.log.json) | +| [glean_ffhq_16x](./glean_x16_2xb8_ffhq.py) | 26.91 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527-61a3afad.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_ffhq_16x_20210527_194536.log.json) | +| [glean_cat_16x](./glean_x16_2xb8_cat.py) | 20.88 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527-68912543.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_cat_16x_20210527_103708.log.json) | +| [glean_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py) | 27.94 | 4 (Tesla V100-SXM3-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812-acbcb04f.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/glean/glean_in128out1024_4x2_300k_ffhq_celebahq_20210812_100549.log.json) | +| [glean_fp16_cat_8x](./glean_x8-fp16_2xb8_cat.py) | - | - | - | +| [glean_fp16_ffhq_16x](./glean_x16-fp16_2xb8_ffhq.py) | - | - | - | +| [glean_fp16_in128out1024_4x2_300k_ffhq_celebahq](./glean_in128out1024-fp16_4xb2-300k_ffhq-celeba-hq.py) | - | - | - | ## 快速开始 diff --git a/configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py b/configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py index 2ecea3757d..ace9f0e59f 100644 --- a/configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py +++ b/configs/glean/glean_in128out1024_4xb2-300k_ffhq-celeba-hq.py @@ -15,7 +15,7 @@ style_channels=512, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-ffhq-config-f-official_20210327' '_171224-bce9310c.pth', prefix='generator_ema')), @@ -24,7 +24,7 @@ in_size=1024, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-ffhq-config-f-official_20210327' '_171224-bce9310c.pth', prefix='discriminator')), diff --git a/configs/glean/glean_x16_2xb8_cat.py b/configs/glean/glean_x16_2xb8_cat.py index 601de1c61e..c90e673b59 100644 --- a/configs/glean/glean_x16_2xb8_cat.py +++ b/configs/glean/glean_x16_2xb8_cat.py @@ -15,7 +15,7 @@ style_channels=512, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-cat-config-f-official_20210327' '_172444-15bc485b.pth', prefix='generator_ema')), @@ -24,7 +24,7 @@ in_size=256, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-cat-config-f-official_20210327' '_172444-15bc485b.pth', prefix='discriminator')), diff --git a/configs/glean/glean_x16_2xb8_ffhq.py b/configs/glean/glean_x16_2xb8_ffhq.py index 84d4d70205..cdd902b8da 100644 --- a/configs/glean/glean_x16_2xb8_ffhq.py +++ b/configs/glean/glean_x16_2xb8_ffhq.py @@ -15,7 +15,7 @@ style_channels=512, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-ffhq-config-f-official_20210327' '_171224-bce9310c.pth', prefix='generator_ema')), @@ -24,7 +24,7 @@ in_size=1024, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-ffhq-config-f-official_20210327' '_171224-bce9310c.pth', prefix='discriminator')), diff --git a/configs/glean/glean_x8_2xb8_cat.py b/configs/glean/glean_x8_2xb8_cat.py index 50023da0e6..e0d501f940 100644 --- a/configs/glean/glean_x8_2xb8_cat.py +++ b/configs/glean/glean_x8_2xb8_cat.py @@ -15,7 +15,7 @@ style_channels=512, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-cat-config-f-official_20210327' '_172444-15bc485b.pth', prefix='generator_ema')), @@ -24,7 +24,7 @@ in_size=256, init_cfg=dict( type='Pretrained', - checkpoint='http://download.openmmlab.com/mmgen/stylegan2/' + checkpoint='http://download.openmmlab.com/mmediting/stylegan2/' 'official_weights/stylegan2-cat-config-f-official_20210327' '_172444-15bc485b.pth', prefix='discriminator')), diff --git a/configs/global_local/README.md b/configs/global_local/README.md index 62067e062e..210ce5b732 100644 --- a/configs/global_local/README.md +++ b/configs/global_local/README.md @@ -24,15 +24,15 @@ We present a novel approach for image completion that results in images that are **Places365-Challenge** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :-------------------------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :---------------------------------------------------------------: | -| [Global&Local](/configs/global_local/gl_8xb12_places-256x256.py) | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :------------------------------------------: | :---------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :----------------------------------------------------------------------------------: | +| [Global&Local](./gl_8xb12_places-256x256.py) | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.log.json) | **CelebA-HQ** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :--------------------------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :-----------------------------------------------------------------: | -| [Global&Local](/configs/global_local/gl_8xb12_celeba-256x256.py) | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :------------------------------------------: | :---------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :-------------------------------------------------------------------------------------: | +| [Global&Local](./gl_8xb12_celeba-256x256.py) | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 8 | [model](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.log.json) | ## Quick Start diff --git a/configs/global_local/README_zh-CN.md b/configs/global_local/README_zh-CN.md index 3836593023..827227a03e 100644 --- a/configs/global_local/README_zh-CN.md +++ b/configs/global_local/README_zh-CN.md @@ -28,15 +28,15 @@ **Places365-Challenge** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :--------------------------------------------------------------: | :---------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------: | -| [Global&Local](/configs/global_local/gl_8xb12_places-256x256.py) | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :------------------------------------------: | :---------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :---------------------------------------------------------------------------------------: | +| [Global&Local](./gl_8xb12_places-256x256.py) | square bbox | 256x256 | 500k | Places365-val | 11.164 | 23.152 | 0.862 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_places_20200619-52a040a8.log.json) | **CelebA-HQ** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :--------------------------------------------------------------: | :---------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :----------------------------------------------------------------------: | -| [Global&Local](/configs/global_local/gl_8xb12_celeba-256x256.py) | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :------------------------------------------: | :---------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :------------------------------------------------------------------------------------------: | +| [Global&Local](./gl_8xb12_celeba-256x256.py) | square bbox | 256x256 | 500k | CelebA-val | 6.678 | 26.780 | 0.904 | 8 | [模型](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/global_local/gl_256x256_8x12_celeba_20200619-5af0493f.log.json) | ## 快速开始 diff --git a/configs/guided_diffusion/README.md b/configs/guided_diffusion/README.md index eb659b8e72..f21407d179 100644 --- a/configs/guided_diffusion/README.md +++ b/configs/guided_diffusion/README.md @@ -22,10 +22,10 @@ We show that diffusion models can achieve image sample quality superior to the c **ImageNet** -| Method | Resolution | Config | Weights | -| ------ | ---------- | ------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------- | -| adm-u | 64x64 | [config](configs/guided_diffusion/adm-u_8xb32_imagenet-64x64.py) | [model](https://download.openmmlab.com/mmgen/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth) | -| adm-u | 512x512 | [config](configs/guided_diffusion/adm-u_8xb32_imagenet-512x512.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmgen/guided_diffusion/adm-u_8xb32_imagenet-512x512-60b381cb.pth) | +| Method | Resolution | Config | Weights | +| ------ | ---------- | ------------------------------------------- | ------------------------------------------------------------------------------------------------------------------ | +| adm-u | 64x64 | [config](./adm-u_8xb32_imagenet-64x64.py) | [model](https://download.openmmlab.com/mmediting/guided_diffusion/adm-u-cvt-rgb_8xb32_imagenet-64x64-7ff0080b.pth) | +| adm-u | 512x512 | [config](./adm-u_8xb32_imagenet-512x512.py) | [model](https://download.openmmlab.com/mmediting/guided_diffusion/adm-u_8xb32_imagenet-512x512-60b381cb.pth) | **Note** To support disco diffusion, we support guided diffusion briefly. Complete support of guided diffusion with metrics and test/train logs will come soom! diff --git a/configs/iconvsr/README.md b/configs/iconvsr/README.md index 2925949748..869e49e76a 100644 --- a/configs/iconvsr/README.md +++ b/configs/iconvsr/README.md @@ -25,15 +25,15 @@ The pretrained weights of the IconVSR components can be found here: [SPyNet](htt | Method | REDS4 (BIx4) PSNR (RGB) | Vimeo-90K-T (BIx4) PSNR (Y) | Vid4 (BIx4) PSNR (Y) | UDM10 (BDx4) PSNR (Y) | Vimeo-90K-T (BDx4) PSNR (Y) | Vid4 (BDx4) PSNR (Y) | GPU Info | Download | | :-----------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :-------------------: | :--------------------: | -| [iconvsr_reds4](/configs/iconvsr/iconvsr_2xb4_reds4.py) | **31.6926** | 36.4983 | **27.4809** | 35.3377 | 34.4299 | 25.2110 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) | -| [iconvsr_vimeo90k_bi](/configs/iconvsr/iconvsr_2xb4_vimeo90k-bi.py) | 30.3452 | **37.3729** | 27.4238 | 34.2595 | 34.5548 | 24.6666 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) | -| [iconvsr_vimeo90k_bd](/configs/iconvsr/iconvsr_2xb4_vimeo90k-bd.py) | 29.0150 | 34.6780 | 26.3109 | **40.0640** | **37.7573** | **28.2464** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) | +| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | **31.6926** | 36.4983 | **27.4809** | 35.3377 | 34.4299 | 25.2110 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) | +| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | 30.3452 | **37.3729** | 27.4238 | 34.2595 | 34.5548 | 24.6666 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) | +| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | 29.0150 | 34.6780 | 26.3109 | **40.0640** | **37.7573** | **28.2464** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) | | Method | REDS4 (BIx4) SSIM (RGB) | Vimeo-90K-T (BIx4) SSIM (Y) | Vid4 (BIx4) SSIM (Y) | UDM10 (BDx4) SSIM (Y) | Vimeo-90K-T (BDx4) SSIM (Y) | Vid4 (BDx4) SSIM (Y) | GPU Info | Download | | :-----------------: | :---------------------: | :-------------------------: | :------------------: | :-------------------: | :-------------------------: | :------------------: | :-------------------: | :--------------------: | -| [iconvsr_reds4](/configs/iconvsr/iconvsr_2xb4_reds4.py) | **0.8951** | 0.9416 | **0.8354** | 0.9471 | 0.9287 | 0.7732 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) | -| [iconvsr_vimeo90k_bi](/configs/iconvsr/iconvsr_2xb4_vimeo90k-bi.py) | 0.8659 | **0.9467** | 0.8297 | 0.9398 | 0.9295 | 0.7491 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) | -| [iconvsr_vimeo90k_bd](/configs/iconvsr/iconvsr_2xb4_vimeo90k-bd.py) | 0.8465 | 0.9339 | 0.8028 | **0.9697** | **0.9517** | **0.8612** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) | +| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | **0.8951** | 0.9416 | **0.8354** | 0.9471 | 0.9287 | 0.7732 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) | +| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | 0.8659 | **0.9467** | 0.8297 | 0.9398 | 0.9295 | 0.7491 | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) | +| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | 0.8465 | 0.9339 | 0.8028 | **0.9697** | **0.9517** | **0.8612** | 2 (Tesla V100-PCIE-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) | ## Quick Start diff --git a/configs/iconvsr/README_zh-CN.md b/configs/iconvsr/README_zh-CN.md index 90c21e71c0..02fe20589a 100644 --- a/configs/iconvsr/README_zh-CN.md +++ b/configs/iconvsr/README_zh-CN.md @@ -25,9 +25,9 @@ IconVSR 组件的预训练权重可以在这里找到：[SPyNet](https://downloa | 算法 | REDS4 (BIx4)
PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4)
PSNR/SSIM (Y) | Vid4 (BIx4)
PSNR/SSIM (Y) | UDM10 (BDx4)
PSNR/SSIM (Y) | Vimeo-90K-T (BDx4)
PSNR/SSIM (Y) | Vid4 (BDx4)
PSNR/SSIM (Y) | GPU 信息 | 下载 | | :-: | :-----------------------------: | :---------------------------------: | :--------------------------: | :---------------------------: | :---------------------------------: | :--------------------------: | :-----: | :--: | -| [iconvsr_reds4](/configs/iconvsr/iconvsr_2xb4_reds4.py) | **31.6926/0.8951** | 36.4983/0.9416 | **27.4809/0.8354** | 35.3377/0.9471 | 34.4299/0.9287 | 25.2110/0.7732 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) | -| [iconvsr_vimeo90k_bi](/configs/iconvsr/iconvsr_2xb4_vimeo90k-bi.py) | 30.3452/0.8659 | **37.3729/0.9467** | 27.4238/0.8297 | 34.2595/0.9398 | 34.5548/0.9295 | 24.6666/0.7491 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) | -| [iconvsr_vimeo90k_bd](/configs/iconvsr/iconvsr_2xb4_vimeo90k-bd.py) | 29.0150/0.8465 | 34.6780/0.9339 | 26.3109/0.8028 | **40.0640/0.9697** | **37.7573/0.9517** | **28.2464/0.8612** | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) | +| [iconvsr_reds4](./iconvsr_2xb4_reds4.py) | **31.6926/0.8951** | 36.4983/0.9416 | **27.4809/0.8354** | 35.3377/0.9471 | 34.4299/0.9287 | 25.2110/0.7732 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-9e09d621.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) | +| [iconvsr_vimeo90k_bi](./iconvsr_2xb4_vimeo90k-bi.py) | 30.3452/0.8659 | **37.3729/0.9467** | 27.4238/0.8297 | 34.2595/0.9398 | 34.5548/0.9295 | 24.6666/0.7491 | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-7c7418dc.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) | +| [iconvsr_vimeo90k_bd](./iconvsr_2xb4_vimeo90k-bd.py) | 29.0150/0.8465 | 34.6780/0.9339 | 26.3109/0.8028 | **40.0640/0.9697** | **37.7573/0.9517** | **28.2464/0.8612** | 2 (Tesla V100-PCIE-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-5f38cb34.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) | ## 快速开始 diff --git a/configs/indexnet/README.md b/configs/indexnet/README.md index 6ea2e18e83..2a44aa751e 100644 --- a/configs/indexnet/README.md +++ b/configs/indexnet/README.md @@ -20,18 +20,18 @@ We show that existing upsampling operators can be unified with the notion of the ## Results and models -| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | -| :--------------------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :---------------------------------------------------------------------------------------: | -| M2O DINs (paper) | 45.8 | 0.013 | 25.9 | **43.7** | - | - | -| [M2O DINs (our)](/configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py) | **45.6** | **0.012** | **25.5** | 44.8 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_20200618_173817.log.json) | +| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | +| :----------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :-------------------------------------------------------------------------------------------------------: | +| M2O DINs (paper) | 45.8 | 0.013 | 25.9 | **43.7** | - | - | +| [M2O DINs (our)](./indexnet_mobv2_1xb16-78k_comp1k.py) | **45.6** | **0.012** | **25.5** | 44.8 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_20200618_173817.log.json) | > The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance. **More result** -| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | -| :------------------------------------------------------------------------------------: | :--: | :---: | :--: | :--: | :------: | :---------------------------------------------------------------------------------------: | -| [M2O DINs (with DIM pipeline)](/configs/indexnet/indexnet_mobv2-dimaug_1xb16-78k_comp1k.py) | 50.1 | 0.016 | 30.8 | 49.5 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_20200626_231857.log.json) | +| Method | SAD | MSE | GRAD | CONN | GPU Info | Download | +| :-------------------------------------------------------------------------: | :--: | :---: | :--: | :--: | :------: | :--------------------------------------------------------------------------------------------------: | +| [M2O DINs (with DIM pipeline)](./indexnet_mobv2-dimaug_1xb16-78k_comp1k.py) | 50.1 | 0.016 | 30.8 | 49.5 | 1 | [model](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth) \| [log](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_20200626_231857.log.json) | ## Quick Start diff --git a/configs/indexnet/README_zh-CN.md b/configs/indexnet/README_zh-CN.md index c54e9a6530..3a3ddd6156 100644 --- a/configs/indexnet/README_zh-CN.md +++ b/configs/indexnet/README_zh-CN.md @@ -20,18 +20,18 @@
-| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | -| :---------------------------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :--------------------------------------------------------------------------------------: | -| M2O DINs (原文) | 45.8 | 0.013 | 25.9 | **43.7** | - | - | -| [M2O DINs (复现)](/configs/indexnet/indexnet_mobv2_1xb16-78k_comp1k.py) | **45.6** | **0.012** | **25.5** | 44.8 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_20200618_173817.log.json) | +| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | +| :-----------------------------------------------------: | :------: | :-------: | :------: | :------: | :------: | :------------------------------------------------------------------------------------------------------: | +| M2O DINs (原文) | 45.8 | 0.013 | 25.9 | **43.7** | - | - | +| [M2O DINs (复现)](./indexnet_mobv2_1xb16-78k_comp1k.py) | **45.6** | **0.012** | **25.5** | 44.8 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_SAD-45.6_20200618_173817-26dd258d.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_mobv2_1x16_78k_comp1k_20200618_173817.log.json) | > The performance of training (best performance) with different random seeds diverges in a large range. You may need to run several experiments for each setting to obtain the above performance. **其他结果** -| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | -| :-------------------------------------------------------------------------------------: | :--: | :---: | :--: | :--: | :------: | :--------------------------------------------------------------------------------------: | -| [M2O DINs (使用 DIM 流水线)](/configs/indexnet/indexnet_mobv2-dimaug_1xb16-78k_comp1k.py) | 50.1 | 0.016 | 30.8 | 49.5 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_20200626_231857.log.json) | +| 算法 | SAD | MSE | GRAD | CONN | GPU 信息 | 下载 | +| :-----------------------------------------------------------------------: | :--: | :---: | :--: | :--: | :------: | :----------------------------------------------------------------------------------------------------: | +| [M2O DINs (使用 DIM 流水线)](./indexnet_mobv2-dimaug_1xb16-78k_comp1k.py) | 50.1 | 0.016 | 30.8 | 49.5 | 1 | [模型](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_SAD-50.1_20200626_231857-af359436.pth) \| [日志](https://download.openmmlab.com/mmediting/mattors/indexnet/indexnet_dimaug_mobv2_1x16_78k_comp1k_20200626_231857.log.json) | ## 快速开始 diff --git a/configs/inst_colorization/README.md b/configs/inst_colorization/README.md index fdfdfb9e50..cc5a5585e8 100644 --- a/configs/inst_colorization/README.md +++ b/configs/inst_colorization/README.md @@ -20,9 +20,9 @@ Image colorization is inherently an ill-posed problem with multi-modal uncertain ## Results and models -| Method | Download | -| :-------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | -| [instance_aware_colorization_officiial](/configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth) | +| Method | Download | +| :----------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | +| [instance_aware_colorization_officiial](./inst-colorizatioon_full_official_cocostuff-256x256.py) | [model](https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth) | ## Quick Start @@ -33,7 +33,7 @@ You can use the following commands to colorize an image. ```shell -python demo/colorization_demo.py configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth input.jpg output.jpg +python demo/colorization_demo.py configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth input.jpg output.jpg ``` For more demos, you can refer to [Tutorial 3: inference with pre-trained models](https://mmediting.readthedocs.io/en/1.x/user_guides/3_inference.html). diff --git a/configs/inst_colorization/README_zh-CN.md b/configs/inst_colorization/README_zh-CN.md index 19e59c64fc..b73dbe46df 100644 --- a/configs/inst_colorization/README_zh-CN.md +++ b/configs/inst_colorization/README_zh-CN.md @@ -20,9 +20,9 @@ Image colorization is inherently an ill-posed problem with multi-modal uncertain ## 结果和模型 -| Method | Download | -| :-------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------: | -| [instance_aware_colorization_officiial](/configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py) | [model](https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth) | +| Method | Download | +| :----------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------: | +| [instance_aware_colorization_officiial](./inst-colorizatioon_full_official_cocostuff-256x256.py) | [model](https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth) | ## 快速开始 @@ -32,7 +32,7 @@ Image colorization is inherently an ill-posed problem with multi-modal uncertain 您可以使用以下命令来对一张图像进行上色。 ```shell -python demo/colorization_demo.py configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth input.jpg output.jpg +python demo/colorization_demo.py configs/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256.py https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth input.jpg output.jpg ``` 更多细节可以参考 [Tutorial 3: inference with pre-trained models](https://mmediting.readthedocs.io/en/1.x/user_guides/3_inference.html)。 diff --git a/configs/inst_colorization/metafile.yml b/configs/inst_colorization/metafile.yml index eec9463695..eee2d2a606 100644 --- a/configs/inst_colorization/metafile.yml +++ b/configs/inst_colorization/metafile.yml @@ -16,4 +16,4 @@ Models: - Dataset: Others Metrics: {} Task: Colorization - Weights: https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth + Weights: https://download.openmmlab.com/mmediting/inst_colorization/inst-colorizatioon_full_official_cocostuff-256x256-5b9d4eee.pth diff --git a/configs/liif/README.md b/configs/liif/README.md index f19b3940ee..d9d0229bde 100644 --- a/configs/liif/README.md +++ b/configs/liif/README.md @@ -22,13 +22,13 @@ How to represent an image? While the visual world is presented in a continuous m | Method | scale | Set5 PSNR | Set5 SSIM | Set14 PSNR | Set14 SSIM | DIV2K PSNR | DIV2K SSIM | GPU Info | Download | | :-----------------------------------------------------------: | :---: | :-------: | :-------: | :--------: | :--------: | :--------: | :--------: | :----------: | :--------------------------------------------------------------: | -| [liif_edsr_norm_c64b16_g1_1000k_div2k](/configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7131 | 0.9366 | 31.5579 | 0.8889 | 34.6647 | 0.9355 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.log.json) | +| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7131 | 0.9366 | 31.5579 | 0.8889 | 34.6647 | 0.9355 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.log.json) | | △ | x3 | 32.3805 | 0.8915 | 28.4605 | 0.8039 | 30.9808 | 0.8724 | △ | △ | | △ | x4 | 30.2748 | 0.8509 | 26.8415 | 0.7381 | 29.0245 | 0.8187 | △ | △ | | △ | x6 | 27.1187 | 0.7774 | 24.7461 | 0.6444 | 26.7770 | 0.7425 | △ | △ | | △ | x18 | 20.8516 | 0.5406 | 20.0096 | 0.4525 | 22.1987 | 0.5955 | △ | △ | | △ | x30 | 18.8467 | 0.5010 | 18.1321 | 0.3963 | 20.5050 | 0.5577 | △ | △ | -| [liif_rdn_norm_c64b16_g1_1000k_div2k](/configs/liif/liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7874 | 0.9366 | 31.6866 | 0.8896 | 34.7548 | 0.9356 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.log.json) | +| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7874 | 0.9366 | 31.6866 | 0.8896 | 34.7548 | 0.9356 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.log.json) | | △ | x3 | 32.4992 | 0.8923 | 28.4905 | 0.8037 | 31.0744 | 0.8731 | △ | △ | | △ | x4 | 30.3835 | 0.8513 | 26.8734 | 0.7373 | 29.1101 | 0.8197 | △ | △ | | △ | x6 | 27.1914 | 0.7751 | 24.7824 | 0.6434 | 26.8693 | 0.7437 | △ | △ | diff --git a/configs/liif/README_zh-CN.md b/configs/liif/README_zh-CN.md index 97fa1da352..e6a21b9e43 100644 --- a/configs/liif/README_zh-CN.md +++ b/configs/liif/README_zh-CN.md @@ -23,13 +23,13 @@ | 算法 | scale | Set5
PSNR / SSIM | Set14
PSNR / SSIM | DIV2K
PSNR / SSIM | GPU 信息 | 下载 | | :-----------------------------------------------------------: | :---: | :-----------------: | :------------------: | :-------------------: | :----------: | :------------------------------------------------------------: | -| [liif_edsr_norm_c64b16_g1_1000k_div2k](/configs/liif/liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7131 / 0.9366 | 31.5579 / 0.8889 | 34.6647 / 0.9355 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.log.json) | +| [liif_edsr_norm_c64b16_g1_1000k_div2k](./liif-edsr-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7131 / 0.9366 | 31.5579 / 0.8889 | 34.6647 / 0.9355 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/liif/liif_edsr_norm_c64b16_g1_1000k_div2k_20210715-ab7ce3fc.log.json) | | △ | x3 | 32.3805 / 0.8915 | 28.4605 / 0.8039 | 30.9808 / 0.8724 | △ | △ | | △ | x4 | 30.2748 / 0.8509 | 26.8415 / 0.7381 | 29.0245 / 0.8187 | △ | △ | | △ | x6 | 27.1187 / 0.7774 | 24.7461 / 0.6444 | 26.7770 / 0.7425 | △ | △ | | △ | x18 | 20.8516 / 0.5406 | 20.0096 / 0.4525 | 22.1987 / 0.5955 | △ | △ | | △ | x30 | 18.8467 / 0.5010 | 18.1321 / 0.3963 | 20.5050 / 0.5577 | △ | △ | -| [liif_rdn_norm_c64b16_g1_1000k_div2k](/configs/liif/liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7874 / 0.9366 | 31.6866 / 0.8896 | 34.7548 / 0.9356 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.log.json) | +| [liif_rdn_norm_c64b16_g1_1000k_div2k](./liif-rdn-norm_c64b16_1xb16-1000k_div2k.py) | x2 | 35.7874 / 0.9366 | 31.6866 / 0.8896 | 34.7548 / 0.9356 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/liif/liif_rdn_norm_c64b16_g1_1000k_div2k_20210717-22d6fdc8.log.json) | | △ | x3 | 32.4992 / 0.8923 | 28.4905 / 0.8037 | 31.0744 / 0.8731 | △ | △ | | △ | x4 | 30.3835 / 0.8513 | 26.8734 / 0.7373 | 29.1101 / 0.8197 | △ | △ | | △ | x6 | 27.1914 / 0.7751 | 24.7824 / 0.6434 | 26.8693 / 0.7437 | △ | △ | @@ -40,7 +40,7 @@ - △ 指同上。 - 这两个配置仅在 _testing pipeline_ 上有所不同。 所以他们使用相同的检查点。 -- 数据根据 [EDSR](/configs/restorers/edsr) 进行正则化。 +- 数据根据 [EDSR](../edsr/README.md) 进行正则化。 - 在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。 ## 快速开始 diff --git a/configs/lsgan/README.md b/configs/lsgan/README.md index f0d8626ef4..75a10b5a60 100644 --- a/configs/lsgan/README.md +++ b/configs/lsgan/README.md @@ -28,10 +28,10 @@ Unsupervised learning with generative adversarial networks (GANs) has proven hug | Models | Dataset | SWD | MS-SSIM | FID | Config | Download | | :-----------: | :------------: | :-----------------------------: | :-----: | :-----: | :-------------------------------------------------------------: | :---------------------------------------------------------------: | -| LSGAN 64x64 | CelebA-Cropped | 6.16, 6.83, 37.64/16.87 | 0.3216 | 11.9258 | [config](/configs/lsgan/lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py) | [model](https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth)\| [log](https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210422_131925.log.json) | -| LSGAN 64x64 | LSUN-Bedroom | 5.66, 9.0, 18.6/11.09 | 0.0671 | 30.7390 | [config](/configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py) | [model](https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth)\| [log](https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210423_005020.log.json) | -| LSGAN 128x128 | CelebA-Cropped | 21.66, 9.83, 16.06, 70.76/29.58 | 0.3691 | 38.3752 | [config](/configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth)\| [log](https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210423_132126.log.json) | -| LSGAN 128x128 | LSUN-Bedroom | 19.52, 9.99, 7.48, 14.3/12.82 | 0.0612 | 51.5500 | [config](/configs/lsgan/lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py) | [model](https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth)\| [log](https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_142302.log.json) | +| LSGAN 64x64 | CelebA-Cropped | 6.16, 6.83, 37.64/16.87 | 0.3216 | 11.9258 | [config](./lsgan_dcgan-archi_lr1e-3-1xb128-12Mimgs_celeba-cropped-64x64.py) | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth)\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210422_131925.log.json) | +| LSGAN 64x64 | LSUN-Bedroom | 5.66, 9.0, 18.6/11.09 | 0.0671 | 30.7390 | [config](./lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py) | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth)\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210423_005020.log.json) | +| LSGAN 128x128 | CelebA-Cropped | 21.66, 9.83, 16.06, 70.76/29.58 | 0.3691 | 38.3752 | [config](./lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth)\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210423_132126.log.json) | +| LSGAN 128x128 | LSUN-Bedroom | 19.52, 9.99, 7.48, 14.3/12.82 | 0.0612 | 51.5500 | [config](./lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py) | [model](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth)\| [log](https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_142302.log.json) | ## Citation diff --git a/configs/lsgan/metafile.yml b/configs/lsgan/metafile.yml index 43aa2678b8..d7ce0fc2f5 100644 --- a/configs/lsgan/metafile.yml +++ b/configs/lsgan/metafile.yml @@ -18,7 +18,7 @@ Models: FID: 11.9258 MS-SSIM: 0.3216 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth + Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-3_64_b128x1_12m_20210429_144001-92ca1d0d.pth - Config: configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb128-12Mimgs_lsun-bedroom-64x64.py In Collection: LSGAN Metadata: @@ -30,7 +30,7 @@ Models: FID: 30.739 MS-SSIM: 0.0671 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth + Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_dcgan-archi_lr-1e-4_64_b128x1_12m_20210429_144602-ec4ec6bb.pth - Config: configs/lsgan/lsgan_dcgan-archi_lr1e-4-1xb64-10Mimgs_celeba-cropped-128x128.py In Collection: LSGAN Metadata: @@ -42,7 +42,7 @@ Models: FID: 38.3752 MS-SSIM: 0.3691 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth + Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_celeba-cropped_dcgan-archi_lr-1e-4_128_b64x1_10m_20210429_144229-01ba67dc.pth - Config: configs/lsgan/lsgan_lsgan-archi_lr1e-4-1xb64-10Mimgs_lsun-bedroom-128x128.py In Collection: LSGAN Metadata: @@ -54,4 +54,4 @@ Models: FID: 51.55 MS-SSIM: 0.0612 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth + Weights: https://download.openmmlab.com/mmediting/lsgan/lsgan_lsun-bedroom_lsgan-archi_lr-1e-4_128_b64x1_10m_20210429_155605-cf78c0a8.pth diff --git a/configs/nafnet/README.md b/configs/nafnet/README.md index e1ec757413..48e9bc872a 100644 --- a/configs/nafnet/README.md +++ b/configs/nafnet/README.md @@ -22,8 +22,8 @@ Although there have been significant advances in the field of image restoration | Method | image size | PSNR | SSIM | GPU Info | Download | | :-------------------------------------------------------------------------: | :--------: | :--------------: | :------------: | :------: | :---------------------------------------------------------------------------: | -| [nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd](/configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py) | 256X256 | 40.3045(40.3045) | 0.9253(0.9614) | 1 (A100) | [model](https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth) \| log(coming soon) | -| [nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro](/configs/nafnet/nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py) | 1280x720 | 33.7246(33.7103) | 0.9479(0.9668) | 1 (A100) | [model](https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth) \| log(coming soon) | +| [nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd](./nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py) | 256X256 | 40.3045(40.3045) | 0.9253(0.9614) | 1 (A100) | [model](https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth) \| log(coming soon) | +| [nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro](./nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py) | 1280x720 | 33.7246(33.7103) | 0.9479(0.9668) | 1 (A100) | [model](https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth) \| log(coming soon) | Note: @@ -51,7 +51,7 @@ python tools/train.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k ./tools/dist_train.sh configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py 8 ``` -For more details, you can refer to **Train a model** part in [train_test.md](/docs/en/user_guides/train_test.md#Train-a-model-in-MMEditing). +For more details, you can refer to **Train a model** part in [train_test.md](../../docs/en/user_guides/train_test.md).
@@ -75,7 +75,7 @@ python tools/test.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_ Pretrained checkpoints will come soon. -For more details, you can refer to **Test a pre-trained model** part in [train_test.md](/docs/en/user_guides/train_test.md#Test-a-pre-trained-model-in-MMEditing). +For more details, you can refer to **Test a pre-trained model** part in [train_test.md](../../docs/en/user_guides/train_test.md).
diff --git a/configs/nafnet/README_zh-CN.md b/configs/nafnet/README_zh-CN.md index f9151c2d61..4c337a8864 100644 --- a/configs/nafnet/README_zh-CN.md +++ b/configs/nafnet/README_zh-CN.md @@ -20,8 +20,8 @@ | 方法 | 图片尺寸 | PSNR | SSIM | GPU信息 | 下载 | | :---------------------------------------------------------------------------: | :------: | :--------------: | :------------: | :------: | :---------------------------------------------------------------------------: | -| [nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd](/configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py) | 256X256 | 40.3045(40.3045) | 0.9253(0.9614) | 1 (A100) | [模型](https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth) \| 日志(即将到来) | -| [nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro](/configs/nafnet/nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py) | 1280x720 | 33.7246(33.7103) | 0.9479(0.9668) | 1 (A100) | [模型](https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth) \| 日志(即将到来) | +| [nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd](./nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py) | 256X256 | 40.3045(40.3045) | 0.9253(0.9614) | 1 (A100) | [模型](https://download.openmmlab.com/mmediting/nafnet/NAFNet-SIDD-midc64.pth) \| 日志(即将到来) | +| [nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro](./nafnet_c64eb11128mb1db1111_8xb8-lr1e-3-400k_gopro.py) | 1280x720 | 33.7246(33.7103) | 0.9479(0.9668) | 1 (A100) | [模型](https://download.openmmlab.com/mmediting/nafnet/NAFNet-GoPro-midc64.pth) \| 日志(即将到来) | Note: @@ -49,7 +49,7 @@ python tools/train.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k ./tools/dist_train.sh configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_sidd.py 8 ``` -更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。 +更多细节可以参考 [train_test.md](../../docs/zh_cn/user_guides/train_test.md) 中的 **Train a model** 部分。 @@ -72,6 +72,6 @@ python tools/test.py configs/nafnet/nafnet_c64eb2248mb12db2222_8xb8-lr1e-3-400k_ ``` 预训练模型未来将会上传，敬请等待。 -更多细节可以参考 [train_test.md](/docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。 +更多细节可以参考 [train_test.md](../../docs/zh_cn/user_guides/train_test.md) 中的 **Test a pre-trained model** 部分。 diff --git a/configs/partial_conv/README.md b/configs/partial_conv/README.md index 0315a12030..9d02a7c33c 100644 --- a/configs/partial_conv/README.md +++ b/configs/partial_conv/README.md @@ -22,17 +22,17 @@ Existing deep learning based image inpainting methods use a standard convolution **Places365-Challenge** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :--------------------------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :----------------------------------------------------------------: | -| [PConv_Stage1](/configs/partial_conv/pconv_stage1_8xb12_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | - | - | - | 8 | - | -| [PConv_Stage2](/configs/partial_conv/pconv_stage2_4xb2_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :----------------------------------------------------: | :-------: | :--------: | :---------: | :-----------: | :------: | :----: | :---: | :------: | :--------------------------------------------------------------------------: | +| [PConv_Stage1](./pconv_stage1_8xb12_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | - | - | - | 8 | - | +| [PConv_Stage2](./pconv_stage2_4xb2_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.log.json) | **CelebA-HQ** -| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | -| :---------------------------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :------------------------------------------------------------------: | -| [PConv_Stage1](/configs/partial_conv/pconv_stage1_8xb1_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | - | - | - | 8 | - | -| [PConv_Stage2](/configs/partial_conv/pconv_stage2_4xb2_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.log.json) | +| Method | Mask Type | Resolution | Train Iters | Test Set | l1 error | PSNR | SSIM | GPU Info | Download | +| :---------------------------------------------------: | :-------: | :--------: | :---------: | :--------: | :------: | :----: | :---: | :------: | :------------------------------------------------------------------------------: | +| [PConv_Stage1](./pconv_stage1_8xb1_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | - | - | - | 8 | - | +| [PConv_Stage2](./pconv_stage2_4xb2_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 4 | [model](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth) \| [log](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.log.json) | ## Quick Start diff --git a/configs/partial_conv/README_zh-CN.md b/configs/partial_conv/README_zh-CN.md index dace459583..3f09969a1d 100644 --- a/configs/partial_conv/README_zh-CN.md +++ b/configs/partial_conv/README_zh-CN.md @@ -23,17 +23,17 @@ **Places365-Challenge** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :-----------------------------------------------------------------: | :-------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :------------------------------------------------------------------: | -| [PConv_Stage1](/configs/partial_conv/pconv_stage1_8xb12_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | - | - | - | 8 | - | -| [PConv_Stage2](/configs/partial_conv/pconv_stage2_4xb2_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :----------------------------------------------------: | :-------: | :-----: | :--------: | :-----------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------------------: | +| [PConv_Stage1](./pconv_stage1_8xb12_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | - | - | - | 8 | - | +| [PConv_Stage2](./pconv_stage2_4xb2_places-256x256.py) | free-form | 256x256 | 500k | Places365-val | 8.776 | 22.762 | 0.801 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_places_20200619-1ffed0e8.log.json) | **CelebA-HQ** -| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | -| :-------------------------------------------------------------------: | :-------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :-------------------------------------------------------------------: | -| [PConv_Stage1](/configs/partial_conv/pconv_stage1_8xb1_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | - | - | - | 8 | - | -| [PConv_Stage2](/configs/partial_conv/pconv_stage2_4xb2_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.log.json) | +| 算法 | 掩膜类型 | 分辨率 | 训练集容量 | 测试集 | l1 损失 | PSNR | SSIM | GPU 信息 | 下载 | +| :---------------------------------------------------: | :-------: | :-----: | :--------: | :--------: | :-----: | :----: | :---: | :------: | :-----------------------------------------------------------------------------------: | +| [PConv_Stage1](./pconv_stage1_8xb1_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | - | - | - | 8 | - | +| [PConv_Stage2](./pconv_stage2_4xb2_celeba-256x256.py) | free-form | 256x256 | 500k | CelebA-val | 5.990 | 25.404 | 0.853 | 4 | [模型](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.pth) \| [日志](https://download.openmmlab.com/mmediting/inpainting/pconv/pconv_256x256_stage2_4x2_celeba_20200619-860f8b95.log.json) | ## 快速开始 diff --git a/configs/pggan/README.md b/configs/pggan/README.md index 6810b50e59..2b8f7316e9 100644 --- a/configs/pggan/README.md +++ b/configs/pggan/README.md @@ -26,11 +26,11 @@ We describe a new training methodology for generative adversarial networks. The -| Models | Details | MS-SSIM | SWD(xx,xx,xx,xx/avg) | Config | Download | -| :-------------: | :------------: | :-----: | :--------------------------: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: | -| pggan_128x128 | celeba-cropped | 0.3023 | 3.42, 4.04, 4.78, 20.38/8.15 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pggan/pggan_8xb4-12Mimgs_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmgen/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth) | -| pggan_128x128 | lsun-bedroom | 0.0602 | 3.5, 2.96, 2.76, 9.65/4.72 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pggan/pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py) | [model](https://download.openmmlab.com/mmgen/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth) | -| pggan_1024x1024 | celeba-hq | 0.3379 | 8.93, 3.98, 3.07, 2.64/4.655 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pggan/pggan_8xb4-12Mimg_celeba-hq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth) | +| Models | Details | MS-SSIM | SWD(xx,xx,xx,xx/avg) | Config | Download | +| :-------------: | :------------: | :-----: | :--------------------------: | :------------------------------------------------------: | :------------------------------------------------------------------------------: | +| pggan_128x128 | celeba-cropped | 0.3023 | 3.42, 4.04, 4.78, 20.38/8.15 | [config](./pggan_8xb4-12Mimgs_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmediting/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth) | +| pggan_128x128 | lsun-bedroom | 0.0602 | 3.5, 2.96, 2.76, 9.65/4.72 | [config](./pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py) | [model](https://download.openmmlab.com/mmediting/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth) | +| pggan_1024x1024 | celeba-hq | 0.3379 | 8.93, 3.98, 3.07, 2.64/4.655 | [config](./pggan_8xb4-12Mimg_celeba-hq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth) | ## Citation diff --git a/configs/pggan/metafile.yml b/configs/pggan/metafile.yml index dfc524f6c4..c8b6f71ca4 100644 --- a/configs/pggan/metafile.yml +++ b/configs/pggan/metafile.yml @@ -17,7 +17,7 @@ Models: Metrics: MS-SSIM: 0.3023 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth + Weights: https://download.openmmlab.com/mmediting/pggan/pggan_celeba-cropped_128_g8_20210408_181931-85a2e72c.pth - Config: configs/pggan/pggan_8xb4-12Mimgs_lsun-bedroom-128x128.py In Collection: PGGAN Metadata: @@ -28,7 +28,7 @@ Models: Metrics: MS-SSIM: 0.0602 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth + Weights: https://download.openmmlab.com/mmediting/pggan/pggan_lsun-bedroom_128x128_g8_20210408_182033-5e59f45d.pth - Config: configs/pggan/pggan_8xb4-12Mimg_celeba-hq-1024x1024.py In Collection: PGGAN Metadata: @@ -39,4 +39,4 @@ Models: Metrics: MS-SSIM: 0.3379 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth + Weights: https://download.openmmlab.com/mmediting/pggan/pggan_celeba-hq_1024_g8_20210408_181911-f1ef51c3.pth diff --git a/configs/pix2pix/README.md b/configs/pix2pix/README.md index 10ceeb3069..3d0d7b4f5a 100644 --- a/configs/pix2pix/README.md +++ b/configs/pix2pix/README.md @@ -27,12 +27,12 @@ We investigate conditional adversarial networks as a general-purpose solution to We use `FID` and `IS` metrics to evaluate the generation performance of pix2pix.1 -| Models | Dataset | FID | IS | Config | Download | -| :----: | :---------: | :------: | :---: | :----------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------: | -| Ours | facades | 124.9773 | 1.620 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py) | [model](https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth) \| [log](https://download.openmmlab.com/mmgen/pix2pix/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210317_172625.log.json)2 | -| Ours | aerial2maps | 122.5856 | 3.137 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py) | [model](https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps_convert-bgr_20210902_170729-59a31517.pth) | -| Ours | maps2aerial | 88.4635 | 3.310 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py) | [model](https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_b2a_1x1_219200_maps_convert-bgr_20210902_170814-6d2eac4a.pth) | -| Ours | edges2shoes | 84.3750 | 2.815 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/pix2pix/pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py) | [model](https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth) | +| Models | Dataset | FID | IS | Config | Download | +| :----: | :---------: | :------: | :---: | :------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------: | +| Ours | facades | 124.9773 | 1.620 | [config](./pix2pix_vanilla-unet-bn_1xb1-80kiters_facades.py) | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth) \| [log](https://download.openmmlab.com/mmediting/pix2pix/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210317_172625.log.json)2 | +| Ours | aerial2maps | 122.5856 | 3.137 | [config](./pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py) | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps_convert-bgr_20210902_170729-59a31517.pth) | +| Ours | maps2aerial | 88.4635 | 3.310 | [config](./pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py) | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_b2a_1x1_219200_maps_convert-bgr_20210902_170814-6d2eac4a.pth) | +| Ours | edges2shoes | 84.3750 | 2.815 | [config](./pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py) | [model](https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth) | `FID` comparison with official: diff --git a/configs/pix2pix/metafile.yml b/configs/pix2pix/metafile.yml index aa96448b7c..1344b00198 100644 --- a/configs/pix2pix/metafile.yml +++ b/configs/pix2pix/metafile.yml @@ -18,7 +18,7 @@ Models: FID: 124.9773 IS: 1.62 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth + Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth - Config: configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_aerial2maps.py In Collection: Pix2Pix Metadata: @@ -30,7 +30,7 @@ Models: FID: 122.5856 IS: 3.137 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps_convert-bgr_20210902_170729-59a31517.pth + Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_a2b_1x1_219200_maps_convert-bgr_20210902_170729-59a31517.pth - Config: configs/pix2pix/pix2pix_vanilla-unet-bn_1xb1-220kiters_maps2aerial.py In Collection: Pix2Pix Metadata: @@ -42,7 +42,7 @@ Models: FID: 88.4635 IS: 3.31 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_b2a_1x1_219200_maps_convert-bgr_20210902_170814-6d2eac4a.pth + Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_b2a_1x1_219200_maps_convert-bgr_20210902_170814-6d2eac4a.pth - Config: configs/pix2pix/pix2pix_vanilla-unet-bn_wo-jitter-flip-1xb4-190kiters_edges2shoes.py In Collection: Pix2Pix Metadata: @@ -54,4 +54,4 @@ Models: FID: 84.375 IS: 2.815 Task: Image2Image - Weights: https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth + Weights: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth diff --git a/configs/positional_encoding_in_gans/README.md b/configs/positional_encoding_in_gans/README.md index 4301a696f5..54d7ed8412 100644 --- a/configs/positional_encoding_in_gans/README.md +++ b/configs/positional_encoding_in_gans/README.md @@ -23,40 +23,40 @@ SinGAN shows impressive capability in learning internal patch distribution despi
896x896 results generated from a 256 generator using MS-PIE
- +
| Models | Reference in Paper | Scales | FID50k | P&R10k | Config | Download | | :--------------------------: | :----------------: | :------------: | :----: | :---------: | :----------------------------------------------------------: | :-------------------------------------------------------------: | -| stylegan2_c2_256_baseline | Tab.5 config-a | 256 | 5.56 | 75.92/51.24 | [stylegan2_c2_8xb3-1100kiters_ffhq-256x256](/configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth) | -| stylegan2_c2_512_baseline | Tab.5 config-b | 512 | 4.91 | 75.65/54.58 | [stylegan2_c2_8xb3-1100kiters_ffhq-512x512](/configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth) | -| ms-pie_stylegan2_c2_config-c | Tab.5 config-c | 256, 384, 512 | 3.35 | 73.84/55.77 | [mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth) | -| ms-pie_stylegan2_c2_config-d | Tab.5 config-d | 256, 384, 512 | 3.50 | 73.28/56.16 | [mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth) | -| ms-pie_stylegan2_c2_config-e | Tab.5 config-e | 256, 384, 512 | 3.15 | 74.13/56.88 | [mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth) | -| ms-pie_stylegan2_c2_config-f | Tab.5 config-f | 256, 384, 512 | 2.93 | 73.51/57.32 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth) | -| ms-pie_stylegan2_c1_config-g | Tab.5 config-g | 256, 384, 512 | 3.40 | 73.05/56.45 | [mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth) | -| ms-pie_stylegan2_c2_config-h | Tab.5 config-h | 256, 384, 512 | 4.01 | 72.81/54.35 | [mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth) | -| ms-pie_stylegan2_c2_config-i | Tab.5 config-i | 256, 384, 512 | 3.76 | 73.26/54.71 | [mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth) | -| ms-pie_stylegan2_c2_config-j | Tab.5 config-j | 256, 384, 512 | 4.23 | 73.11/54.63 | [mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth) | -| ms-pie_stylegan2_c2_config-k | Tab.5 config-k | 256, 384, 512 | 4.17 | 73.05/51.07 | [mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth) | -| ms-pie_stylegan2_c2_config-f | higher-resolution | 256, 512, 896 | 4.10 | 72.21/50.29 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896](/configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth) | -| ms-pie_stylegan2_c1_config-f | higher-resolution | 256, 512, 1024 | 6.24 | 71.79/49.92 | [mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024](/configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth) | +| stylegan2_c2_256_baseline | Tab.5 config-a | 256 | 5.56 | 75.92/51.24 | [stylegan2_c2_8xb3-1100kiters_ffhq-256x256](./stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth) | +| stylegan2_c2_512_baseline | Tab.5 config-b | 512 | 4.91 | 75.65/54.58 | [stylegan2_c2_8xb3-1100kiters_ffhq-512x512](./stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth) | +| ms-pie_stylegan2_c2_config-c | Tab.5 config-c | 256, 384, 512 | 3.35 | 73.84/55.77 | [mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth) | +| ms-pie_stylegan2_c2_config-d | Tab.5 config-d | 256, 384, 512 | 3.50 | 73.28/56.16 | [mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth) | +| ms-pie_stylegan2_c2_config-e | Tab.5 config-e | 256, 384, 512 | 3.15 | 74.13/56.88 | [mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth) | +| ms-pie_stylegan2_c2_config-f | Tab.5 config-f | 256, 384, 512 | 2.93 | 73.51/57.32 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth) | +| ms-pie_stylegan2_c1_config-g | Tab.5 config-g | 256, 384, 512 | 3.40 | 73.05/56.45 | [mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth) | +| ms-pie_stylegan2_c2_config-h | Tab.5 config-h | 256, 384, 512 | 4.01 | 72.81/54.35 | [mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth) | +| ms-pie_stylegan2_c2_config-i | Tab.5 config-i | 256, 384, 512 | 3.76 | 73.26/54.71 | [mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth) | +| ms-pie_stylegan2_c2_config-j | Tab.5 config-j | 256, 384, 512 | 4.23 | 73.11/54.63 | [mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth) | +| ms-pie_stylegan2_c2_config-k | Tab.5 config-k | 256, 384, 512 | 4.17 | 73.05/51.07 | [mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth) | +| ms-pie_stylegan2_c2_config-f | higher-resolution | 256, 512, 896 | 4.10 | 72.21/50.29 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth) | +| ms-pie_stylegan2_c1_config-f | higher-resolution | 256, 512, 1024 | 6.24 | 71.79/49.92 | [mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024](./mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth) | | Models | Reference in Paper | Scales | FID50k | Precision10k | Recall10k | Config | Download | | :--------------------------: | :----------------: | :------------: | :----: | :----------: | :-------: | :-----------------------------------------------------: | :--------------------------------------------------------: | -| stylegan2_c2_256_baseline | Tab.5 config-a | 256 | 5.56 | 75.92 | 51.24 | [stylegan2_c2_8xb3-1100kiters_ffhq-256x256](/configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth) | -| stylegan2_c2_512_baseline | Tab.5 config-b | 512 | 4.91 | 75.65 | 54.58 | [stylegan2_c2_8xb3-1100kiters_ffhq-512x512](/configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth) | -| ms-pie_stylegan2_c2_config-c | Tab.5 config-c | 256, 384, 512 | 3.35 | 73.84 | 55.77 | [mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth) | -| ms-pie_stylegan2_c2_config-d | Tab.5 config-d | 256, 384, 512 | 3.50 | 73.28 | 56.16 | [mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth) | -| ms-pie_stylegan2_c2_config-e | Tab.5 config-e | 256, 384, 512 | 3.15 | 74.13 | 56.88 | [mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth) | -| ms-pie_stylegan2_c2_config-f | Tab.5 config-f | 256, 384, 512 | 2.93 | 73.51 | 57.32 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth) | -| ms-pie_stylegan2_c1_config-g | Tab.5 config-g | 256, 384, 512 | 3.40 | 73.05 | 56.45 | [mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth) | -| ms-pie_stylegan2_c2_config-h | Tab.5 config-h | 256, 384, 512 | 4.01 | 72.81 | 54.35 | [mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth) | -| ms-pie_stylegan2_c2_config-i | Tab.5 config-i | 256, 384, 512 | 3.76 | 73.26 | 54.71 | [mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth) | -| ms-pie_stylegan2_c2_config-j | Tab.5 config-j | 256, 384, 512 | 4.23 | 73.11 | 54.63 | [mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth) | -| ms-pie_stylegan2_c2_config-k | Tab.5 config-k | 256, 384, 512 | 4.17 | 73.05 | 51.07 | [mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512](/configs/positional_encoding_in_gans/mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth) | -| ms-pie_stylegan2_c2_config-f | higher-resolution | 256, 512, 896 | 4.10 | 72.21 | 50.29 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896](/configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth) | -| ms-pie_stylegan2_c1_config-f | higher-resolution | 256, 512, 1024 | 6.24 | 71.79 | 49.92 | [mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024](/configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py) | [model](https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth) | +| stylegan2_c2_256_baseline | Tab.5 config-a | 256 | 5.56 | 75.92 | 51.24 | [stylegan2_c2_8xb3-1100kiters_ffhq-256x256](./stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth) | +| stylegan2_c2_512_baseline | Tab.5 config-b | 512 | 4.91 | 75.65 | 54.58 | [stylegan2_c2_8xb3-1100kiters_ffhq-512x512](./stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth) | +| ms-pie_stylegan2_c2_config-c | Tab.5 config-c | 256, 384, 512 | 3.35 | 73.84 | 55.77 | [mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth) | +| ms-pie_stylegan2_c2_config-d | Tab.5 config-d | 256, 384, 512 | 3.50 | 73.28 | 56.16 | [mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth) | +| ms-pie_stylegan2_c2_config-e | Tab.5 config-e | 256, 384, 512 | 3.15 | 74.13 | 56.88 | [mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth) | +| ms-pie_stylegan2_c2_config-f | Tab.5 config-f | 256, 384, 512 | 2.93 | 73.51 | 57.32 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth) | +| ms-pie_stylegan2_c1_config-g | Tab.5 config-g | 256, 384, 512 | 3.40 | 73.05 | 56.45 | [mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth) | +| ms-pie_stylegan2_c2_config-h | Tab.5 config-h | 256, 384, 512 | 4.01 | 72.81 | 54.35 | [mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth) | +| ms-pie_stylegan2_c2_config-i | Tab.5 config-i | 256, 384, 512 | 3.76 | 73.26 | 54.71 | [mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth) | +| ms-pie_stylegan2_c2_config-j | Tab.5 config-j | 256, 384, 512 | 4.23 | 73.11 | 54.63 | [mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth) | +| ms-pie_stylegan2_c2_config-k | Tab.5 config-k | 256, 384, 512 | 4.17 | 73.05 | 51.07 | [mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512](./mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth) | +| ms-pie_stylegan2_c2_config-f | higher-resolution | 256, 512, 896 | 4.10 | 72.21 | 50.29 | [mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896](./mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth) | +| ms-pie_stylegan2_c1_config-f | higher-resolution | 256, 512, 1024 | 6.24 | 71.79 | 49.92 | [mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024](./mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py) | [model](https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth) | Note that we report the FID and P&R metric (FFHQ dataset) in the largest scale. @@ -70,14 +70,14 @@ Note that we report the FID and P&R metric (FFHQ dataset) in the largest scale. | Model | Data | Num Scales | Config | Download | | :-----------------------------: | :-------------------------------------------------: | :--------: | :---------------------------------------------------: | :-----------------------------------------------------: | -| SinGAN + no pad | [balloons.png](https://download.openmmlab.com/mmgen/dataset/singan/balloons.png) | 8 | [singan_interp-pad_balloons](/configs/positional_encoding_in_gans/singan_interp-pad_balloons.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pkl) | -| SinGAN + no pad + no bn in disc | [balloons.png](https://download.openmmlab.com/mmgen/dataset/singan/balloons.png) | 8 | [singan_interp-pad_disc-nobn_balloons](/configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_balloons.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pkl) | -| SinGAN + no pad + no bn in disc | [fish.jpg](https://download.openmmlab.com/mmgen/dataset/singan/fish-crop.jpg) | 10 | [singan_interp-pad_disc-nobn_fish](/configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_fish.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pkl) | -| SinGAN + CSG | [fish.jpg](https://download.openmmlab.com/mmgen/dataset/singan/fish-crop.jpg) | 10 | [singan-csg_fish](/configs/positional_encoding_in_gans/singan-csg_fish.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pkl) | -| SinGAN + CSG | [bohemian.png](https://download.openmmlab.com/mmgen/dataset/singan/bohemian.png) | 10 | [singan-csg_bohemian](/configs/positional_encoding_in_gans/singan-csg_bohemian.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pkl) | -| SinGAN + SPE-dim4 | [fish.jpg](https://download.openmmlab.com/mmgen/dataset/singan/fish-crop.jpg) | 10 | [singan_spe-dim4_fish](/configs/positional_encoding_in_gans/singan_spe-dim4_fish.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pkl) | -| SinGAN + SPE-dim4 | [bohemian.png](https://download.openmmlab.com/mmgen/dataset/singan/bohemian.png) | 10 | [singan_spe-dim4_bohemian](/configs/positional_encoding_in_gans/singan_spe-dim4_bohemian.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pkl) | -| SinGAN + SPE-dim8 | [bohemian.png](https://download.openmmlab.com/mmgen/dataset/singan/bohemian.png) | 10 | [singan_spe-dim8_bohemian](/configs/positional_encoding_in_gans/singan_spe-dim8_bohemian.py) | [ckpt](https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pth) \| [pkl](https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pkl) | +| SinGAN + no pad | [balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png) | 8 | [singan_interp-pad_balloons](./singan_interp-pad_balloons.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pkl) | +| SinGAN + no pad + no bn in disc | [balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png) | 8 | [singan_interp-pad_disc-nobn_balloons](./singan_interp-pad_disc-nobn_balloons.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pkl) | +| SinGAN + no pad + no bn in disc | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [singan_interp-pad_disc-nobn_fish](./singan_interp-pad_disc-nobn_fish.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pkl) | +| SinGAN + CSG | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [singan-csg_fish](./singan-csg_fish.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pkl) | +| SinGAN + CSG | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [singan-csg_bohemian](./singan-csg_bohemian.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pkl) | +| SinGAN + SPE-dim4 | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [singan_spe-dim4_fish](./singan_spe-dim4_fish.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pkl) | +| SinGAN + SPE-dim4 | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [singan_spe-dim4_bohemian](./singan_spe-dim4_bohemian.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pkl) | +| SinGAN + SPE-dim8 | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [singan_spe-dim8_bohemian](./singan_spe-dim8_bohemian.py) | [ckpt](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pth) \| [pkl](https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pkl) | ## Citation diff --git a/configs/positional_encoding_in_gans/metafile.yml b/configs/positional_encoding_in_gans/metafile.yml index d649dc171c..8cbea69b6d 100644 --- a/configs/positional_encoding_in_gans/metafile.yml +++ b/configs/positional_encoding_in_gans/metafile.yml @@ -21,7 +21,7 @@ Models: SSIM: 51.24 Scales: 256.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth - Config: configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py In Collection: Positional Encoding in GANs Metadata: @@ -36,7 +36,7 @@ Models: SSIM: 54.58 Scales: 512.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -50,7 +50,7 @@ Models: PSNR: 73.84 SSIM: 55.77 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -64,7 +64,7 @@ Models: PSNR: 73.28 SSIM: 56.16 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -78,7 +78,7 @@ Models: PSNR: 74.13 SSIM: 56.88 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -92,7 +92,7 @@ Models: PSNR: 73.51 SSIM: 57.32 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -106,7 +106,7 @@ Models: PSNR: 73.05 SSIM: 56.45 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -120,7 +120,7 @@ Models: PSNR: 72.81 SSIM: 54.35 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -134,7 +134,7 @@ Models: PSNR: 73.26 SSIM: 54.71 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -148,7 +148,7 @@ Models: PSNR: 73.11 SSIM: 54.63 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -162,7 +162,7 @@ Models: PSNR: 73.05 SSIM: 51.07 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py In Collection: Positional Encoding in GANs Metadata: @@ -176,7 +176,7 @@ Models: PSNR: 72.21 SSIM: 50.29 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py In Collection: Positional Encoding in GANs Metadata: @@ -190,7 +190,7 @@ Models: PSNR: 71.79 SSIM: 49.92 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth - Config: configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-256x256.py In Collection: Positional Encoding in GANs Metadata: @@ -204,7 +204,7 @@ Models: Recall10k: 51.24 Scales: 256.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-a_ffhq_256x256_b3x8_1100k_20210406_145127-71d9634b.pth - Config: configs/positional_encoding_in_gans/stylegan2_c2_8xb3-1100kiters_ffhq-512x512.py In Collection: Positional Encoding in GANs Metadata: @@ -218,7 +218,7 @@ Models: Recall10k: 54.58 Scales: 512.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/stylegan2_c2_config-b_ffhq_512x512_b3x8_1100k_20210406_145142-e85e5cf4.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-c_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -231,7 +231,7 @@ Models: Precision10k: 73.84 Recall10k: 55.77 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-c_ffhq_256-512_b3x8_1100k_20210406_144824-9f43b07d.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-d_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -244,7 +244,7 @@ Models: Precision10k: 73.28 Recall10k: 56.16 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-d_ffhq_256-512_b3x8_1100k_20210406_144840-dbefacf6.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-e_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -257,7 +257,7 @@ Models: Precision10k: 74.13 Recall10k: 56.88 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-e_ffhq_256-512_b3x8_1100k_20210406_144906-98d5a42a.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -270,7 +270,7 @@ Models: Precision10k: 73.51 Recall10k: 57.32 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-512_b3x8_1100k_20210406_144927-4f4d5391.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-g_c1_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -283,7 +283,7 @@ Models: Precision10k: 73.05 Recall10k: 56.45 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-g_ffhq_256-512_b3x8_1100k_20210406_144758-2df61752.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-h_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -296,7 +296,7 @@ Models: Precision10k: 72.81 Recall10k: 54.35 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-h_ffhq_256-512_b3x8_1100k_20210406_145006-84cf3f48.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-i_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -309,7 +309,7 @@ Models: Precision10k: 73.26 Recall10k: 54.71 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-i_ffhq_256-512_b3x8_1100k_20210406_145023-c2b0accf.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-j_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -322,7 +322,7 @@ Models: Precision10k: 73.11 Recall10k: 54.63 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-j_ffhq_256-512_b3x8_1100k_20210406_145044-c407481b.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-k_c2_8xb3-1100kiters_ffhq-256-512.py In Collection: Positional Encoding in GANs Metadata: @@ -335,7 +335,7 @@ Models: Precision10k: 73.05 Recall10k: 51.07 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-k_ffhq_256-512_b3x8_1100k_20210406_145105-6d8cc39f.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c2_8xb3-1100kiters_ffhq-256-896.py In Collection: Positional Encoding in GANs Metadata: @@ -348,7 +348,7 @@ Models: Precision10k: 72.21 Recall10k: 50.29 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c2_config-f_ffhq_256-896_b3x8_1100k_20210406_144943-6c18ad5d.pth - Config: configs/positional_encoding_in_gans/mspie-stylegan2-config-f_c1_8xb2-1600kiters_ffhq-256-1024.py In Collection: Positional Encoding in GANs Metadata: @@ -361,7 +361,7 @@ Models: Precision10k: 71.79 Recall10k: 49.92 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/mspie-stylegan2_c1_config-f_ffhq_256-1024_b2x8_1600k_20210406_144716-81cbdc96.pth - Config: configs/positional_encoding_in_gans/singan_interp-pad_balloons.py In Collection: Positional Encoding in GANs Metadata: @@ -372,7 +372,7 @@ Models: Metrics: Num Scales: 8.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_balloons_20210406_180014-96f51555.pth - Config: configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_balloons.py In Collection: Positional Encoding in GANs Metadata: @@ -383,7 +383,7 @@ Models: Metrics: Num Scales: 8.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_balloons_20210406_180059-7d63e65d.pth - Config: configs/positional_encoding_in_gans/singan_interp-pad_disc-nobn_fish.py In Collection: Positional Encoding in GANs Metadata: @@ -394,7 +394,7 @@ Models: Metrics: Num Scales: 10.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_interp-pad_disc-nobn_fis_20210406_175720-9428517a.pth - Config: configs/positional_encoding_in_gans/singan-csg_fish.py In Collection: Positional Encoding in GANs Metadata: @@ -405,7 +405,7 @@ Models: Metrics: Num Scales: 10.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_fis_20210406_175532-f0ec7b61.pth - Config: configs/positional_encoding_in_gans/singan-csg_bohemian.py In Collection: Positional Encoding in GANs Metadata: @@ -416,7 +416,7 @@ Models: Metrics: Num Scales: 10.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_csg_bohemian_20210407_195455-5ed56db2.pth - Config: configs/positional_encoding_in_gans/singan_spe-dim4_fish.py In Collection: Positional Encoding in GANs Metadata: @@ -427,7 +427,7 @@ Models: Metrics: Num Scales: 10.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_fish_20210406_175933-f483a7e3.pth - Config: configs/positional_encoding_in_gans/singan_spe-dim4_bohemian.py In Collection: Positional Encoding in GANs Metadata: @@ -438,7 +438,7 @@ Models: Metrics: Num Scales: 10.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim4_bohemian_20210406_175820-6e484a35.pth - Config: configs/positional_encoding_in_gans/singan_spe-dim8_bohemian.py In Collection: Positional Encoding in GANs Metadata: @@ -449,4 +449,4 @@ Models: Metrics: Num Scales: 10.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pth + Weights: https://download.openmmlab.com/mmediting/pe_in_gans/singan_spe-dim8_bohemian_20210406_175858-7faa50f3.pth diff --git a/configs/rdn/README.md b/configs/rdn/README.md index 88a05ec7a3..827bc90192 100644 --- a/configs/rdn/README.md +++ b/configs/rdn/README.md @@ -25,9 +25,9 @@ The metrics are `PSNR and SSIM` . | Method | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU Info | Download | | :--------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :----------: | :----------------------------------------------------------------: | -| [rdn_x4c64b16_g1_1000k_div2k](/configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py) | 30.4922 | 26.9570 | 29.1925 | 0.8548 | 0.7423 | 0.8233 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) | -| [rdn_x3c64b16_g1_1000k_div2k](/configs/rdn/rdn_x3c64b16_1xb16-1000k_div2k.py) | 32.6051 | 28.6338 | 31.2153 | 0.8943 | 0.8077 | 0.8763 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) | -| [rdn_x2c64b16_g1_1000k_div2k](/configs/rdn/rdn_x2c64b16_1xb16-1000k_div2k.py) | 35.9883 | 31.8366 | 34.9392 | 0.9385 | 0.8920 | 0.9380 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) | +| [rdn_x4c64b16_g1_1000k_div2k](./rdn_x4c64b16_1xb16-1000k_div2k.py) | 30.4922 | 26.9570 | 29.1925 | 0.8548 | 0.7423 | 0.8233 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) | +| [rdn_x3c64b16_g1_1000k_div2k](./rdn_x3c64b16_1xb16-1000k_div2k.py) | 32.6051 | 28.6338 | 31.2153 | 0.8943 | 0.8077 | 0.8763 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) | +| [rdn_x2c64b16_g1_1000k_div2k](./rdn_x2c64b16_1xb16-1000k_div2k.py) | 35.9883 | 31.8366 | 34.9392 | 0.9385 | 0.8920 | 0.9380 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) | ## Quick Start diff --git a/configs/rdn/README_zh-CN.md b/configs/rdn/README_zh-CN.md index e183d7b650..77082fa95d 100644 --- a/configs/rdn/README_zh-CN.md +++ b/configs/rdn/README_zh-CN.md @@ -24,11 +24,11 @@ 在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。 我们使用 `PSNR` 和 `SSIM` 作为指标。 -| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | -| :--------------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :----------: | :--------------------------------------------------------------------: | -| [rdn_x2c64b16_g1_1000k_div2k](/configs/rdn/rdn_x2c64b16_1xb16-1000k_div2k.py) | 35.9883 / 0.9385 | 31.8366 / 0.8920 | 34.9392 / 0.9380 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) | -| [rdn_x3c64b16_g1_1000k_div2k](/configs/rdn/rdn_x3c64b16_1xb16-1000k_div2k.py) | 32.6051 / 0.8943 | 28.6338 / 0.8077 | 31.2153 / 0.8763 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) | -| [rdn_x4c64b16_g1_1000k_div2k](/configs/rdn/rdn_x4c64b16_1xb16-1000k_div2k.py) | 30.4922 / 0.8548 | 26.9570 / 0.7423 | 29.1925 / 0.8233 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) | +| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | +| :----------------------------------------------------------------: | :--------------: | :--------------: | :--------------: | :----------: | :------------------------------------------------------------------------: | +| [rdn_x2c64b16_g1_1000k_div2k](./rdn_x2c64b16_1xb16-1000k_div2k.py) | 35.9883 / 0.9385 | 31.8366 / 0.8920 | 34.9392 / 0.9380 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x2c64b16_g1_1000k_div2k_20210419-dc146009.log.json) | +| [rdn_x3c64b16_g1_1000k_div2k](./rdn_x3c64b16_1xb16-1000k_div2k.py) | 32.6051 / 0.8943 | 28.6338 / 0.8077 | 31.2153 / 0.8763 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x3c64b16_g1_1000k_div2k_20210419-b93cb6aa.log.json) | +| [rdn_x4c64b16_g1_1000k_div2k](./rdn_x4c64b16_1xb16-1000k_div2k.py) | 30.4922 / 0.8548 | 26.9570 / 0.7423 | 29.1925 / 0.8233 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/rdn/rdn_x4c64b16_g1_1000k_div2k_20210419-3577d44f.log.json) | ## 快速开始 diff --git a/configs/real_basicvsr/README.md b/configs/real_basicvsr/README.md index 60a662a6a0..7b0e6c6e7a 100644 --- a/configs/real_basicvsr/README.md +++ b/configs/real_basicvsr/README.md @@ -24,8 +24,8 @@ Evaluated on Y channel. The code for computing NRQM, NIQE, and PI can be found [ | Method | NRQM (Y) | NIQE (Y) | PI (Y) | BRISQUE (Y) | GPU Info | Download | | :--------------------------------------------------------------------: | :------: | :------: | :----: | :---------: | :----------------------: | :-----------------------------------------------------------------------: | -| [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds](/configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) | 6.0477 | 3.7662 | 3.8593 | 29.030 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth)/[log](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104_183640.log.json) | -| [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds](/configs/real_basicvsr/realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth)/[log](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027_114039.log.json) | +| [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds](./realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) | 6.0477 | 3.7662 | 3.8593 | 29.030 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth)/[log](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104_183640.log.json) | +| [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds](./realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth)/[log](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027_114039.log.json) | ## Quick Start diff --git a/configs/real_basicvsr/README_zh-CN.md b/configs/real_basicvsr/README_zh-CN.md index 45d40cb39f..e753988416 100644 --- a/configs/real_basicvsr/README_zh-CN.md +++ b/configs/real_basicvsr/README_zh-CN.md @@ -24,19 +24,19 @@ | 算法 | NRQM (Y) | NIQE (Y) | PI (Y) | BRISQUE (Y) | GPU 信息 | Download | | :------------------------------------------------------------------: | :------: | :------: | :----: | :---------: | :----------------------: | :-------------------------------------------------------------------------: | -| [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds](/configs/real_basicvsr/realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) | 6.0477 | 3.7662 | 3.8593 | 29.030 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth)/[log](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104_183640.log.json) | -| [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds](/configs/real_basicvsr/realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | [model](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth)/[log](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027_114039.log.json) | +| [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds](./realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) | 6.0477 | 3.7662 | 3.8593 | 29.030 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104-52f77c2c.pth)/[log](https://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds_20211104_183640.log.json) | +| [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds](./realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | [model](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027-0e2ff207.pth)/[log](http://download.openmmlab.com/mmediting/restorers/real_basicvsr/realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds_20211027_114039.log.json) | ## 训练 训练分为两个阶段： -1. 使用 [realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds.py](realbasicvsr_wogan_c64b20_2x30x8_lr1e-4_300k_reds.py) 训练一个没有感知损失和对抗性损失的模型。 -2. 使用感知损失和对抗性损失 [realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds.py](realbasicvsr_c64b20_1x30x8_lr5e-5_150k_reds.py) 微调模型。 +1. 使用 [realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py](realbasicvsr_wogan-c64b20-2x30x8_8xb2-lr1e-4-300k_reds.py) 训练一个没有感知损失和对抗性损失的模型。 +2. 使用感知损失和对抗性损失 [realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py ](realbasicvsr_c64b20-1x30x8_8xb1-lr5e-5-150k_reds.py) 微调模型。 **注:** -1. 您可能希望将图像裁剪为子图像以加快 IO。请参阅[此处](/tools/data/super-resolution/reds/preprocess_reds_dataset.py)了解更多详情。 +1. 您可能希望将图像裁剪为子图像以加快 IO。请参阅[此处](../../tools/dataset_converters/reds/preprocess_reds_dataset.py)了解更多详情。 ## 快速开始 diff --git a/configs/real_esrgan/README.md b/configs/real_esrgan/README.md index 596ccad510..b908beda44 100644 --- a/configs/real_esrgan/README.md +++ b/configs/real_esrgan/README.md @@ -24,8 +24,8 @@ Evaluated on Set5 dataset with RGB channels. The metrics are `PSNR` and `SSIM`. | Method | PSNR | SSIM | GPU Info | Download | | :------------------------------------------------------------------------------: | :-----: | :----: | :----------------------: | :---------------------------------------------------------------------------------: | -| [realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost](/configs/real_esrgan/realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py) | 28.0297 | 0.8236 | 4 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth)/log | -| [realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost](/configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py) | 26.2204 | 0.7655 | 4 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth) /[log](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20210922_142838.log.json) | +| [realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost](./realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py) | 28.0297 | 0.8236 | 4 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth)/log | +| [realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost](./realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py) | 26.2204 | 0.7655 | 4 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth) /[log](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20210922_142838.log.json) | ## Quick Start diff --git a/configs/real_esrgan/README_zh-CN.md b/configs/real_esrgan/README_zh-CN.md index 54485dd2de..c35f4ac571 100644 --- a/configs/real_esrgan/README_zh-CN.md +++ b/configs/real_esrgan/README_zh-CN.md @@ -25,8 +25,8 @@ | 算法 | Set5 | GPU 信息 | 下载 | | :-------------------------------------------------------------------------------: | :------------: | :----------------------: | :-------------------------------------------------------------------------------: | -| [realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost](/configs/real_esrgan/realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py) | 28.0297/0.8236 | 4 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth)/日志 | -| [realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost](/configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py) | 26.2204/0.7655 | 4 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth) /[日志](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20210922_142838.log.json) | +| [realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost](./realesrnet_c64b23g32_4xb12-lr2e-4-1000k_df2k-ost.py) | 28.0297/0.8236 | 4 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrnet_c64b23g32_12x4_lr2e-4_1000k_df2k_ost_20210816-4ae3b5a4.pth)/日志 | +| [realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost](./realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py) | 26.2204/0.7655 | 4 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20211010-34798885.pth) /[日志](https://download.openmmlab.com/mmediting/restorers/real_esrgan/realesrgan_c64b23g32_12x4_lr1e-4_400k_df2k_ost_20210922_142838.log.json) | ## 快速开始 diff --git a/configs/restormer/README.md b/configs/restormer/README.md index 2bc7c1f5c8..32d0cca049 100644 --- a/configs/restormer/README.md +++ b/configs/restormer/README.md @@ -26,7 +26,7 @@ Evaluated on Y channels. The metrics are `PSNR` / `SSIM` . | Method | Rain100H
PSNR/SSIM (Y) | Rain100L
PSNR/SSIM (Y) | Test100
PSNR/SSIM (Y) | Test1200
PSNR/SSIM (Y) | Test2800
PSNR/SSIM (Y) | GPU Info | Download | | :-------------------------------: | :-----------------------: | :-----------------------: | :----------------------: | :-----------------------: | :-----------------------: | :------: | :---------------------------------: | -| [restormer_official_rain13k](/configs/restormer/restormer_official_rain13k.py) | 31.4804/0.9056 | 39.1023/0.9787 | 32.0287/0.9239 | 33.2251/0.9272 | 34.2170/0.9451 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \| log | +| [restormer_official_rain13k](./restormer_official_rain13k.py) | 31.4804/0.9056 | 39.1023/0.9787 | 32.0287/0.9239 | 33.2251/0.9272 | 34.2170/0.9451 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \| log | ### **Motion Deblurring** @@ -34,16 +34,16 @@ Evaluated on RGB channels for GoPro and HIDE, and Y channel for ReakBlur-J and R | Method | GoPro
PSNR/SSIM (RGB) | HIDE
PSNR/SSIM (RGB) | RealBlur-J
PSNR/SSIM (Y) | RealBlur-R
PSNR/SSIM (Y) | GPU Info | Download | | :------------------------------------------: | :----------------------: | :---------------------: | :-------------------------: | :-------------------------: | :------: | :---------------------------------------------: | -| [restormer_official_gopro](/configs/restormer/restormer_official_gopro.py) | 32.9295/0.9496 | 31.2289/0.9345 | 28.4356/0.8681 | 35.9141/0.9707 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \| log | +| [restormer_official_gopro](./restormer_official_gopro.py) | 32.9295/0.9496 | 31.2289/0.9345 | 28.4356/0.8681 | 35.9141/0.9707 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \| log | ### **Defocus Deblurring** Evaluated on RGB channels. The metrics are `PSNR` / `SSIM` / `MAE` / `LPIPS`. -| Method | Indoor Scenes PSNR | Indoor Scenes SSIM | Indoor Scenes MAE | Indoor Scenes LPIPS | Outdoor Scenes PSNR | Outdoor Scenes SSIM | Outdoor Scenes MAE | Outdoor Scenes LPIPS | Combined PSNR | Combined SSIM | Combined MAE | Combined LPIPS | GPU Info | Download | -| :------------------------------------------------------------------------------------: | :----------------: | :----------------: | :---------------: | :-----------------: | :-----------------: | :-----------------: | :----------------: | :------------------: | :-----------: | :-----------: | :----------: | :------------: | :------: | :------------------------------------------------------------------------------------------------------------: | -| [restormer_official_dpdd-single](/configs/restormer/restormer_official_dpdd-single.py) | 28.8681 | 0.8859 | 0.0251 | - | 23.2410 | 0.7509 | 0.0499 | - | 25.9805 | 0.8166 | 0.0378 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \| log | -| [restormer_official_dpdd-dual](/configs/restormer/restormer_official_dpdd-dual.py) | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth) \| log | +| Method | Indoor Scenes PSNR | Indoor Scenes SSIM | Indoor Scenes MAE | Indoor Scenes LPIPS | Outdoor Scenes PSNR | Outdoor Scenes SSIM | Outdoor Scenes MAE | Outdoor Scenes LPIPS | Combined PSNR | Combined SSIM | Combined MAE | Combined LPIPS | GPU Info | Download | +| :-------------------------------------------------------------------: | :----------------: | :----------------: | :---------------: | :-----------------: | :-----------------: | :-----------------: | :----------------: | :------------------: | :-----------: | :-----------: | :----------: | :------------: | :------: | :------------------------------------------------------------------------------------------------------------: | +| [restormer_official_dpdd-single](./restormer_official_dpdd-single.py) | 28.8681 | 0.8859 | 0.0251 | - | 23.2410 | 0.7509 | 0.0499 | - | 25.9805 | 0.8166 | 0.0378 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \| log | +| [restormer_official_dpdd-dual](./restormer_official_dpdd-dual.py) | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth) \| log | ### **Gaussian Denoising** @@ -53,13 +53,13 @@ Evaluated on grayscale images. The metrics are `PSNR` / `SSIM` . | Method | $\\sigma$ | Set12
PSNR/SSIM | BSD68
PSNR/SSIM | Urban100
PSNR/SSIM | GPU Info | Download | | :------------------------------------------------------------: | :-------: | :----------------: | :----------------: | :-------------------: | :------: | :--------------------------------------------------------------: | -| [restormer_official_dfwb-gray-sigma15](/configs/restormer/restormer_official_dfwb-gray-sigma15.py) | 15 | 34.0182/0.9160 | 32.4987/0.8940 | 34.4336/0.9419 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) \| log | -| [restormer_official_dfwb-gray-sigma25](/configs/restormer/restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7289/0.8811 | 30.1613/0.8370 | 32.1162/0.9140 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) \| log | -| [restormer_official_dfwb-gray-sigma50](/configs/restormer/restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6269/0.8188 | 27.3266/0.7434 | 28.9636/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) \| log | +| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | 15 | 34.0182/0.9160 | 32.4987/0.8940 | 34.4336/0.9419 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) \| log | +| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7289/0.8811 | 30.1613/0.8370 | 32.1162/0.9140 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) \| log | +| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6269/0.8188 | 27.3266/0.7434 | 28.9636/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) \| log | | | | | | | | | -| [restormer_official_dfwb-gray-sigma15](/configs/restormer/restormer_official_dfwb-gray-sigma15.py) | 15 | 33.9642/0.9153 | 32.4994/0.8928 | 34.3152/0.9409 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | -| [restormer_official_dfwb-gray-sigma25](/configs/restormer/restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7106/0.8810 | 30.1486/0.8360 | 32.0457/0.9131 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | -| [restormer_official_dfwb-gray-sigma50](/configs/restormer/restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6614/0.8197 | 27.3537/0.7422 | 28.9848/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | +| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | 15 | 33.9642/0.9153 | 32.4994/0.8928 | 34.3152/0.9409 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | +| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7106/0.8810 | 30.1486/0.8360 | 32.0457/0.9131 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | +| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6614/0.8197 | 27.3537/0.7422 | 28.9848/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | > Top super-row: training a separate model for each noise level. Bottom super-row: learning a single model to handle various noise levels. @@ -69,13 +69,13 @@ Evaluated on RGB channels. The metrics are `PSNR` / `SSIM` . | Method | $\\sigma$ | CBSD68
PSNR/SSIM (RGB) | Kodak24
PSNR/SSIM (RGB) | McMaster
PSNR/SSIM (RGB) | Urban100
PSNR/SSIM (RGB) | GPU Info | Download | | :------------------------------------: | :-------: | :-----------------------: | :------------------------: | :-------------------------: | :-------------------------: | :------: | :--------------------------------------: | -| [restormer_official_dfwb-color-sigma15](/configs/restormer/restormer_official_dfwb-color-sigma15.py) | 15 | 34.3506/0.9352 | 35.4900/0.9312 | 35.6072/0.9352 | 35.1522/0.9530 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \| log | -| [restormer_official_dfwb-color-sigma25](/configs/restormer/restormer_official_dfwb-color-sigma25.py) | 25 | 31.7457/0.8942 | 33.0489/0.8943 | 33.3260/0.9066 | 32.9670/0.9317 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \| log | -| [restormer_official_dfwb-color-sigma50](/configs/restormer/restormer_official_dfwb-color-sigma50.py) | 50 | 28.5569/0.8127 | 30.0122/0.8238 | 30.2608/0.8515 | 30.0230/0.8902 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \| log | +| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | 15 | 34.3506/0.9352 | 35.4900/0.9312 | 35.6072/0.9352 | 35.1522/0.9530 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \| log | +| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | 25 | 31.7457/0.8942 | 33.0489/0.8943 | 33.3260/0.9066 | 32.9670/0.9317 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \| log | +| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | 50 | 28.5569/0.8127 | 30.0122/0.8238 | 30.2608/0.8515 | 30.0230/0.8902 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \| log | | | | | | | | | | -| [restormer_official_dfwb-color-sigma15](/configs/restormer/restormer_official_dfwb-color-sigma15.py) | 15 | 34.3422/0.9356 | 35.4544/0.9308 | 35.5473/0.9344 | 35.0754/0.9524 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | -| [restormer_official_dfwb-color-sigma25](/configs/restormer/restormer_official_dfwb-color-sigma25.py) | 25 | 31.7391/0.8945 | 33.0380/0.8941 | 33.3040/0.9063 | 32.9165/0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | -| [restormer_official_dfwb-color-sigma50](/configs/restormer/restormer_official_dfwb-color-sigma50.py) | 50 | 28.5582/0.8126 | 30.0074/0.8233 | 30.2671/0.8520 | 30.0172/0.8898 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | +| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | 15 | 34.3422/0.9356 | 35.4544/0.9308 | 35.5473/0.9344 | 35.0754/0.9524 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | +| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | 25 | 31.7391/0.8945 | 33.0380/0.8941 | 33.3040/0.9063 | 32.9165/0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | +| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | 50 | 28.5582/0.8126 | 30.0074/0.8233 | 30.2671/0.8520 | 30.0172/0.8898 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | > Top super-row: training a separate model for each noise level. Bottom super-row: learning a single model to handle various noise levels. @@ -83,9 +83,9 @@ Evaluated on RGB channels. The metrics are `PSNR` / `SSIM` . Evaluated on RGB channels. The metrics are `PSNR` / `SSIM` . -| Method | SIDD
PSNR/SSIM | GPU Info | Download | -| :----------------------------------------------------------------------: | :---------------: | :------: | :-----------------------------------------------------------------------------------------------------: | -| [restormer_official_sidd](/configs/restormer/restormer_official_sidd.py) | 40.0156/0.9225 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth) \| log | +| Method | SIDD
PSNR/SSIM | GPU Info | Download | +| :-----------------------------------------------------: | :---------------: | :------: | :-----------------------------------------------------------------------------------------------------: | +| [restormer_official_sidd](./restormer_official_sidd.py) | 40.0156/0.9225 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth) \| log | ## Quick Start @@ -114,7 +114,7 @@ CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_officia # Single CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth # Dual -CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth +CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth # Gaussian Denoising # Test Grayscale Gaussian Noise @@ -160,7 +160,7 @@ python tools/test.py configs/restormer/restormer_official_gopro.py https://downl # Single python tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth # Dual -python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth +python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth # Gaussian Denoising # Test Grayscale Gaussian Noise @@ -207,7 +207,7 @@ python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py # Single ./tools/dist_test.sh configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth # Dual -./tools/dist_test.sh configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth +./tools/dist_test.sh configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth # Gaussian Denoising # Test Grayscale Gaussian Noise diff --git a/configs/restormer/README_zh-CN.md b/configs/restormer/README_zh-CN.md index 400f597d62..91c669f492 100644 --- a/configs/restormer/README_zh-CN.md +++ b/configs/restormer/README_zh-CN.md @@ -18,7 +18,7 @@ | 方法 | Rain100H
PSNR/SSIM (Y) | Rain100L
PSNR/SSIM (Y) | Test100
PSNR/SSIM (Y) | Test1200
PSNR/SSIM (Y) | Test2800
PSNR/SSIM (Y) | GPU信息 | 下载 | | :--------------------------------: | :-----------------------: | :-----------------------: | :----------------------: | :-----------------------: | :-----------------------: | :-----: | :---------------------------------: | -| [restormer_official_rain13k](/configs/restormer/restormer_official_rain13k.py) | 31.4804/0.9056 | 39.1023/0.9787 | 32.0287/0.9239 | 33.2251/0.9272 | 34.2170/0.9451 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \| log | +| [restormer_official_rain13k](./restormer_official_rain13k.py) | 31.4804/0.9056 | 39.1023/0.9787 | 32.0287/0.9239 | 33.2251/0.9272 | 34.2170/0.9451 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_rain13k-2be7b550.pth) \| log | ### **图像去模糊** @@ -26,7 +26,7 @@ Gopro和HIDE数据集上使用RGB通道测试，ReakBlur-J 和 ReakBlur-R数据 | 方法 | GoPro
PSNR/SSIM (RGB) | HIDE
PSNR/SSIM (RGB) | RealBlur-J
PSNR/SSIM (Y) | RealBlur-R
PSNR/SSIM (Y) | GPU信息 | 下载 | | :--------------------------------------------: | :----------------------: | :---------------------: | :-------------------------: | :-------------------------: | :-----: | :--------------------------------------------: | -| [restormer_official_gopro](/configs/restormer/restormer_official_gopro.py) | 32.9295/0.9496 | 31.2289/0.9345 | 28.4356/0.8681 | 35.9141/0.9707 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \| log | +| [restormer_official_gopro](./restormer_official_gopro.py) | 32.9295/0.9496 | 31.2289/0.9345 | 28.4356/0.8681 | 35.9141/0.9707 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_gopro-db7363a0.pth) \| log | ### **图像去失焦模糊** @@ -34,8 +34,8 @@ Gopro和HIDE数据集上使用RGB通道测试，ReakBlur-J 和 ReakBlur-R数据 | 方法 | 室内场景图像的PSNR | 室内场景图像的SSIM | 室内场景图像的MAE | 室内场景图像的LPIPS | 室外场景图像的PSNR | 室外场景图像的SSIM | 室外场景图像的MAE | 室外场景图像的LPIPS | 所有图像平均PSNR | 所有图像平均SSIM | 所有图像平均MAE | 所有图像平均LPIPS | GPU 信息 | 下载 | | :----: | :-------------: | :-------------: | :------------: | :--------------: | :-------------: | :-------------: | :------------: | :--------------: | :------------: | :-------------: | :------------: | :--------------: | :------: | :-----: | -| [restormer_official_dpdd-single](/configs/restormer/restormer_official_dpdd-single.py) | 28.8681 | 0.8859 | 0.0251 | - | 23.2410 | 0.7509 | 0.0499 | - | 25.9805 | 0.8166 | 0.0378 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \| log | -| [restormer_official_dpdd-dual](/configs/restormer/restormer_official_dpdd-dual.py) | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth) \| log | +| [restormer_official_dpdd-single](./restormer_official_dpdd-single.py) | 28.8681 | 0.8859 | 0.0251 | - | 23.2410 | 0.7509 | 0.0499 | - | 25.9805 | 0.8166 | 0.0378 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth) \| log | +| [restormer_official_dpdd-dual](./restormer_official_dpdd-dual.py) | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 26.6160 | 0.8346 | 0.0354 | - | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth) \| log | ### **图像高斯噪声去除** @@ -45,13 +45,13 @@ Gopro和HIDE数据集上使用RGB通道测试，ReakBlur-J 和 ReakBlur-R数据 | 方法 | $\\sigma$ | Set12
PSNR/SSIM | BSD68
PSNR/SSIM | Urban100
PSNR/SSIM | GPU信息 | 下载 | | :-------------------------------------------------------------: | :-------: | :----------------: | :----------------: | :-------------------: | :-----: | :--------------------------------------------------------------: | -| [restormer_official_dfwb-gray-sigma15](/configs/restormer/restormer_official_dfwb-gray-sigma15.py) | 15 | 34.0182/0.9160 | 32.4987/0.8940 | 34.4336/0.9419 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) \| log | -| [restormer_official_dfwb-gray-sigma25](/configs/restormer/restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7289/0.8811 | 30.1613/0.8370 | 32.1162/0.9140 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) \| log | -| [restormer_official_dfwb-gray-sigma50](/configs/restormer/restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6269/0.8188 | 27.3266/0.7434 | 28.9636/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) \| log | +| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | 15 | 34.0182/0.9160 | 32.4987/0.8940 | 34.4336/0.9419 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma15-da74417f.pth) \| log | +| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7289/0.8811 | 30.1613/0.8370 | 32.1162/0.9140 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma25-08010841.pth) \| log | +| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6269/0.8188 | 27.3266/0.7434 | 28.9636/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-sigma50-ee852dfe.pth) \| log | | | | | | | | | -| [restormer_official_dfwb-gray-sigma15](/configs/restormer/restormer_official_dfwb-gray-sigma15.py) | 15 | 33.9642/0.9153 | 32.4994/0.8928 | 34.3152/0.9409 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | -| [restormer_official_dfwb-gray-sigma25](/configs/restormer/restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7106/0.8810 | 30.1486/0.8360 | 32.0457/0.9131 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | -| [restormer_official_dfwb-gray-sigma50](/configs/restormer/restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6614/0.8197 | 27.3537/0.7422 | 28.9848/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | +| [restormer_official_dfwb-gray-sigma15](./restormer_official_dfwb-gray-sigma15.py) | 15 | 33.9642/0.9153 | 32.4994/0.8928 | 34.3152/0.9409 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | +| [restormer_official_dfwb-gray-sigma25](./restormer_official_dfwb-gray-sigma25.py) | 25 | 31.7106/0.8810 | 30.1486/0.8360 | 32.0457/0.9131 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | +| [restormer_official_dfwb-gray-sigma50](./restormer_official_dfwb-gray-sigma50.py) | 50 | 28.6614/0.8197 | 27.3537/0.7422 | 28.9848/0.8571 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-gray-blind-5f094bcc.pth) \| log | > 上面三行代表每个噪声等级训练一个单独的模型，下面三行代表学习一个单一的模型来处理各种噪音水平。 @@ -61,13 +61,13 @@ Gopro和HIDE数据集上使用RGB通道测试，ReakBlur-J 和 ReakBlur-R数据 | 方法 | $\\sigma$ | CBSD68
PSNR/SSIM (RGB) | Kodak24
PSNR/SSIM (RGB) | McMaster
PSNR/SSIM (RGB) | Urban100
PSNR/SSIM (RGB) | GPU信息 | 下载 | | :-------------------------------------: | :-------: | :-----------------------: | :------------------------: | :-------------------------: | :-------------------------: | :-----: | :--------------------------------------: | -| [restormer_official_dfwb-color-sigma15](/configs/restormer/restormer_official_dfwb-color-sigma15.py) | 15 | 34.3506/0.9352 | 35.4900/0.9312 | 35.6072/0.9352 | 35.1522/0.9530 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \| log | -| [restormer_official_dfwb-color-sigma25](/configs/restormer/restormer_official_dfwb-color-sigma25.py) | 25 | 31.7457/0.8942 | 33.0489/0.8943 | 33.3260/0.9066 | 32.9670/0.9317 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \| log | -| [restormer_official_dfwb-color-sigma50](/configs/restormer/restormer_official_dfwb-color-sigma50.py) | 50 | 28.5569/0.8127 | 30.0122/0.8238 | 30.2608/0.8515 | 30.0230/0.8902 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \| log | +| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | 15 | 34.3506/0.9352 | 35.4900/0.9312 | 35.6072/0.9352 | 35.1522/0.9530 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma15-012ceb71.pth) \| log | +| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | 25 | 31.7457/0.8942 | 33.0489/0.8943 | 33.3260/0.9066 | 32.9670/0.9317 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma25-e307f222.pth) \| log | +| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | 50 | 28.5569/0.8127 | 30.0122/0.8238 | 30.2608/0.8515 | 30.0230/0.8902 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-sigma50-a991983d.pth) \| log | | | | | | | | | | -| [restormer_official_dfwb-color-sigma15](/configs/restormer/restormer_official_dfwb-color-sigma15.py) | 15 | 34.3422/0.9356 | 35.4544/0.9308 | 35.5473/0.9344 | 35.0754/0.9524 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | -| [restormer_official_dfwb-color-sigma25](/configs/restormer/restormer_official_dfwb-color-sigma25.py) | 25 | 31.7391/0.8945 | 33.0380/0.8941 | 33.3040/0.9063 | 32.9165/0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | -| [restormer_official_dfwb-color-sigma50](/configs/restormer/restormer_official_dfwb-color-sigma50.py) | 50 | 28.5582/0.8126 | 30.0074/0.8233 | 30.2671/0.8520 | 30.0172/0.8898 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | +| [restormer_official_dfwb-color-sigma15](./restormer_official_dfwb-color-sigma15.py) | 15 | 34.3422/0.9356 | 35.4544/0.9308 | 35.5473/0.9344 | 35.0754/0.9524 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | +| [restormer_official_dfwb-color-sigma25](./restormer_official_dfwb-color-sigma25.py) | 25 | 31.7391/0.8945 | 33.0380/0.8941 | 33.3040/0.9063 | 32.9165/0.9312 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | +| [restormer_official_dfwb-color-sigma50](./restormer_official_dfwb-color-sigma50.py) | 50 | 28.5582/0.8126 | 30.0074/0.8233 | 30.2671/0.8520 | 30.0172/0.8898 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_dfwb-color-blind-dfd03c9f.pth) \| log | > 上面三行代表每个噪声等级训练一个单独的模型，下面三行代表学习一个单一的模型来处理各种噪音水平。 @@ -75,9 +75,9 @@ Gopro和HIDE数据集上使用RGB通道测试，ReakBlur-J 和 ReakBlur-R数据 所有指标均在RGB通道上进行测试，测试指标为PSNR和SSIM。 -| 方法 | SIDD
PSNR/SSIM | GPU信息 | 下载 | -| :----------------------------------------------------------------------: | :---------------: | :-----: | :-----------------------------------------------------------------------------------------------------: | -| [restormer_official_sidd](/configs/restormer/restormer_official_sidd.py) | 40.0156/0.9225 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth) \| log | +| 方法 | SIDD
PSNR/SSIM | GPU信息 | 下载 | +| :-----------------------------------------------------: | :---------------: | :-----: | :-----------------------------------------------------------------------------------------------------: | +| [restormer_official_sidd](./restormer_official_sidd.py) | 40.0156/0.9225 | 1 | [model](https://download.openmmlab.com/mmediting/restormer/restormer_official_sidd-9e7025db.pth) \| log | ## 使用方法 @@ -104,7 +104,7 @@ CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_officia # Single CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth # Dual -CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth +CUDA_VISIBLE_DEVICES=-1 python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth # Gaussian Denoising # Test Grayscale Gaussian Noise @@ -150,7 +150,7 @@ python tools/test.py configs/restormer/restormer_official_gopro.py https://downl # Single python tools/test.py configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth # Dual -python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth +python tools/test.py configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth # Gaussian Denoising # Test Grayscale Gaussian Noise @@ -197,7 +197,7 @@ python tools/test.py configs/restormer/restormer_official_dfwb-color-sigma50.py # Single ./tools/dist_test.sh configs/restormer/restormer_official_dpdd-dual.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-single-6bc31582.pth # Dual -./tools/dist_test.sh configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth +./tools/dist_test.sh configs/restormer/restormer_official_dpdd-single.py https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth # Gaussian Denoising # Test Grayscale Gaussian Noise diff --git a/configs/restormer/metafile.yml b/configs/restormer/metafile.yml index 90e8f0b8a1..dbb62c29f7 100644 --- a/configs/restormer/metafile.yml +++ b/configs/restormer/metafile.yml @@ -95,7 +95,7 @@ Models: Outdoor Scenes PSNR: 26.616 Outdoor Scenes SSIM: 0.8346 Task: Denoising, Deblurring, Deraining - Weights: https://download.openmmlab.com/mmediting/restormer_official_dpdd-dual-52c94c00.pth + Weights: https://download.openmmlab.com/mmediting/restormer/restormer_official_dpdd-dual-52c94c00.pth - Config: configs/restormer/restormer_official_dfwb-gray-sigma15.py In Collection: Restormer Metadata: diff --git a/configs/sagan/README.md b/configs/sagan/README.md index 6e4a3cf5f9..ddb9d96149 100644 --- a/configs/sagan/README.md +++ b/configs/sagan/README.md @@ -28,14 +28,14 @@ In this paper, we propose the Self-Attention Generative Adversarial Network (SAG | Models | Dataset | Inplace ReLU | dist_step | Total Batchsize (BZ_PER_GPU * NGPU) | Total Iters\* | Iter | IS | FID | Config | Download | Log | | :------------------------: | :------: | :----------: | :-------: | :---------------------------------: | :-----------: | :----: | :-----: | :-----: | :------------------------: | :--------------------------: | :---------------------: | -| SAGAN-32x32-woInplaceReLU Best IS | CIFAR10 | w/o | 5 | 64x1 | 500000 | 400000 | 9.3217 | 10.5030 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_is-iter400000_20210730_125743-4008a9ca.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_20210730_125449_fid-d50568a4_is-04008a9ca.json) | -| SAGAN-32x32-woInplaceReLU Best FID | CIFAR10 | w/o | 5 | 64x1 | 500000 | 480000 | 9.3174 | 9.4252 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_20210730_125449_fid-d50568a4_is-04008a9ca.json) | -| SAGAN-32x32-wInplaceReLU Best IS | CIFAR10 | w | 5 | 64x1 | 500000 | 380000 | 9.2286 | 11.7760 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_is-iter380000_20210730_124937-c77b4d25.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_20210730_125155_fid-cbefb354_is-c77b4d25.json) | -| SAGAN-32x32-wInplaceReLU Best FID | CIFAR10 | w | 5 | 64x1 | 500000 | 460000 | 9.2061 | 10.7781 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_20210730_125155_fid-cbefb354_is-c77b4d25.json) | -| SAGAN-128x128-woInplaceReLU Best IS | ImageNet | w/o | 1 | 64x4 | 1000000 | 980000 | 31.5938 | 36.7712 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_is-iter980000_20210730_163140-cfbebfc6.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_20210730_163431_fid-d7916963_is-cfbebfc6.json) | -| SAGAN-128x128-woInplaceReLU Best FID | ImageNet | w/o | 1 | 64x4 | 1000000 | 950000 | 28.4936 | 34.7838 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_20210730_163431_fid-d7916963_is-cfbebfc6.json) | -| SAGAN-128x128-BigGAN Schedule Best IS | ImageNet | w/o | 1 | 32x8 | 1000000 | 826000 | 69.5350 | 12.8295 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.json) | -| SAGAN-128x128-BigGAN Schedule Best FID | ImageNet | w/o | 1 | 32x8 | 1000000 | 826000 | 69.5350 | 12.8295 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth) | [Log](https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.json) | +| SAGAN-32x32-woInplaceReLU Best IS | CIFAR10 | w/o | 5 | 64x1 | 500000 | 400000 | 9.3217 | 10.5030 | [config](./sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_is-iter400000_20210730_125743-4008a9ca.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_20210730_125449_fid-d50568a4_is-04008a9ca.json) | +| SAGAN-32x32-woInplaceReLU Best FID | CIFAR10 | w/o | 5 | 64x1 | 500000 | 480000 | 9.3174 | 9.4252 | [config](./sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_20210730_125449_fid-d50568a4_is-04008a9ca.json) | +| SAGAN-32x32-wInplaceReLU Best IS | CIFAR10 | w | 5 | 64x1 | 500000 | 380000 | 9.2286 | 11.7760 | [config](./sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_is-iter380000_20210730_124937-c77b4d25.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_20210730_125155_fid-cbefb354_is-c77b4d25.json) | +| SAGAN-32x32-wInplaceReLU Best FID | CIFAR10 | w | 5 | 64x1 | 500000 | 460000 | 9.2061 | 10.7781 | [config](./sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_20210730_125155_fid-cbefb354_is-c77b4d25.json) | +| SAGAN-128x128-woInplaceReLU Best IS | ImageNet | w/o | 1 | 64x4 | 1000000 | 980000 | 31.5938 | 36.7712 | [config](./sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_is-iter980000_20210730_163140-cfbebfc6.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_20210730_163431_fid-d7916963_is-cfbebfc6.json) | +| SAGAN-128x128-woInplaceReLU Best FID | ImageNet | w/o | 1 | 64x4 | 1000000 | 950000 | 28.4936 | 34.7838 | [config](./sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_20210730_163431_fid-d7916963_is-cfbebfc6.json) | +| SAGAN-128x128-BigGAN Schedule Best IS | ImageNet | w/o | 1 | 32x8 | 1000000 | 826000 | 69.5350 | 12.8295 | [config](./sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.json) | +| SAGAN-128x128-BigGAN Schedule Best FID | ImageNet | w/o | 1 | 32x8 | 1000000 | 826000 | 69.5350 | 12.8295 | [config](./sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth) | [Log](https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.json) | '\*' Iteration counting rule in our implementation is different from others. If you want to align with other codebases, you can use the following conversion formula: @@ -48,8 +48,8 @@ To be noted that, in Pytorch Studio GAN, **inplace ReLU** is used in generator a | Models | Dataset | Inplace ReLU | n_disc | Total Iters | IS (Our Pipeline) | FID (Our Pipeline) | IS (StudioGAN) | FID (StudioGAN) | Config | Download | Original Download link | | :------------------: | :------: | :----------: | :----: | :---------: | :---------------: | :----------------: | :------------: | :-------------: | :------------------: | :--------------------: | :-----------------------------------: | -| SAGAN-32x32 StudioGAN | CIFAR10 | w | 5 | 100000 | 9.116 | 10.2011 | 8.680 | 14.009 | [Config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_cvt-studioGAN_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth) | [model](https://drive.google.com/drive/folders/1FA8hcz4MB8-hgTwLuDA0ZUfr8slud5P_) | -| SAGAN0-128x128 StudioGAN | ImageNet | w | 1 | 1000000 | 27.367 | 40.1162 | 29.848 | 34.726 | [Config](https://github.com/open-mmlab/mmediting/tree/master/configs/sagan/sagan_128_cvt_studioGAN.py) | [model](https://download.openmmlab.com/mmgen/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth) | [model](https://drive.google.com/drive/folders/1ZYaqeeumDgxOPDhRR5QLeLFIpgBJ9S6B) | +| SAGAN-32x32 StudioGAN | CIFAR10 | w | 5 | 100000 | 9.116 | 10.2011 | 8.680 | 14.009 | [Config](./sagan_cvt-studioGAN_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth) | [model](https://drive.google.com/drive/folders/1FA8hcz4MB8-hgTwLuDA0ZUfr8slud5P_) | +| SAGAN0-128x128 StudioGAN | ImageNet | w | 1 | 1000000 | 27.367 | 40.1162 | 29.848 | 34.726 | [Config](./sagan_128_cvt_studioGAN.py) | [model](https://download.openmmlab.com/mmediting/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth) | [model](https://drive.google.com/drive/folders/1ZYaqeeumDgxOPDhRR5QLeLFIpgBJ9S6B) | - `Our Pipeline` denote results evaluated with our pipeline. - `StudioGAN` denote results released by Pytorch-StudioGAN. @@ -61,7 +61,7 @@ For IS metric, our implementation is different from PyTorch-Studio GAN in the fo For FID evaluation, we follow the pipeline of [BigGAN](https://github.com/ajbrock/BigGAN-PyTorch/blob/98459431a5d618d644d54cd1e9fceb1e5045648d/calculate_inception_moments.py#L52), where the whole training set is adopted to extract inception statistics, and Pytorch Studio GAN uses 50000 randomly selected samples. Besides, we also use [Tero's Inception](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for feature extraction. -You can download the preprocessed inception state by the following url: [CIFAR10](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/cifar10.pkl) and [ImageNet1k](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/imagenet.pkl). +You can download the preprocessed inception state by the following url: [CIFAR10](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/cifar10.pkl) and [ImageNet1k](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/imagenet.pkl). You can use following commands to extract those inception states by yourself. diff --git a/configs/sagan/metafile.yml b/configs/sagan/metafile.yml index ee04fa7f3a..be7836aaf5 100644 --- a/configs/sagan/metafile.yml +++ b/configs/sagan/metafile.yml @@ -21,7 +21,7 @@ Models: Total Iters\*: 500000.0 dist_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_is-iter400000_20210730_125743-4008a9ca.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_is-iter400000_20210730_125743-4008a9ca.pth - Config: configs/sagan/sagan_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py In Collection: SAGAN Metadata: @@ -36,7 +36,7 @@ Models: Total Iters\*: 500000.0 dist_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_woReUinplace_fid-iter480000_20210730_125449-d50568a4.pth - Config: configs/sagan/sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py In Collection: SAGAN Metadata: @@ -51,7 +51,7 @@ Models: Total Iters\*: 500000.0 dist_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_is-iter380000_20210730_124937-c77b4d25.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_is-iter380000_20210730_124937-c77b4d25.pth - Config: configs/sagan/sagan_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py In Collection: SAGAN Metadata: @@ -66,7 +66,7 @@ Models: Total Iters\*: 500000.0 dist_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_cifar10_32_lr2e-4_ndisc5_b64x1_wReLUinplace_fid-iter460000_20210730_125155-cbefb354.pth - Config: configs/sagan/sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py In Collection: SAGAN Metadata: @@ -81,7 +81,7 @@ Models: Total Iters\*: 1000000.0 dist_step: 1.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_is-iter980000_20210730_163140-cfbebfc6.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_is-iter980000_20210730_163140-cfbebfc6.pth - Config: configs/sagan/sagan_woReLUinplace_Glr1e-4_Dlr4e-4_ndisc1-4xb64_imagenet1k-128x128.py In Collection: SAGAN Metadata: @@ -96,7 +96,7 @@ Models: Total Iters\*: 1000000.0 dist_step: 1.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_imagenet1k_128_Glr1e-4_Dlr4e-4_ndisc1_b32x4_woReLUinplace_fid-iter950000_20210730_163431-d7916963.pth - Config: configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py In Collection: SAGAN Metadata: @@ -111,7 +111,7 @@ Models: Total Iters\*: 1000000.0 dist_step: 1.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth - Config: configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py In Collection: SAGAN Metadata: @@ -126,7 +126,7 @@ Models: Total Iters\*: 1000000.0 dist_step: 1.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth - Config: configs/sagan/sagan_cvt-studioGAN_cifar10-32x32.py In Collection: SAGAN Metadata: @@ -142,7 +142,7 @@ Models: Total Iters: 100000.0 n_disc: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_32_cifar10_convert-studio-rgb_20210730_153321-080da7e2.pth - Config: configs/sagan/sagan_128_cvt_studioGAN.py In Collection: SAGAN Metadata: @@ -158,4 +158,4 @@ Models: Total Iters: 1000000.0 n_disc: 1.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth + Weights: https://download.openmmlab.com/mmediting/sagan/sagan_128_imagenet1k_convert-studio-rgb_20210730_153357-eddb0d1d.pth diff --git a/configs/singan/README.md b/configs/singan/README.md index 7d13fe333f..e39785f5c3 100644 --- a/configs/singan/README.md +++ b/configs/singan/README.md @@ -26,11 +26,11 @@ We introduce SinGAN, an unconditional generative model that can be learned from -| Models | Data | Num Scales | Config | Download | -| :----: | :---------------------------------------------------------: | :--------: | :-----------------------------------------------------------: | :--------------------------------------------------------------: | -| SinGAN | [balloons.png](https://download.openmmlab.com/mmgen/dataset/singan/balloons.png) | 8 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/singan/singan_balloons.py) | [ckpt](https://download.openmmlab.com/mmgen/singan/singan_balloons_20210406_191047-8fcd94cf.pth) \| [pkl](https://download.openmmlab.com/mmgen/singan/singan_balloons_20210406_191047-8fcd94cf.pkl) | -| SinGAN | [fish.jpg](https://download.openmmlab.com/mmgen/dataset/singan/fish-crop.jpg) | 10 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/singan/singan_fish.py) | [ckpt](https://download.openmmlab.com/mmgen/singan/singan_fis_20210406_201006-860d91b6.pth) \| [pkl](https://download.openmmlab.com/mmgen/singan/singan_fis_20210406_201006-860d91b6.pkl) | -| SinGAN | [bohemian.png](https://download.openmmlab.com/mmgen/dataset/singan/bohemian.png) | 10 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/singan/singan_bohemian.py) | [ckpt](https://download.openmmlab.com/mmgen/singan/singan_bohemian_20210406_175439-f964ee38.pth) \| [pkl](https://download.openmmlab.com/mmgen/singan/singan_bohemian_20210406_175439-f964ee38.pkl) | +| Models | Data | Num Scales | Config | Download | +| :----: | :-------------------------------------------------------------------------: | :--------: | :----------------------------: | :-----------------------------------------------------------------------------: | +| SinGAN | [balloons.png](https://download.openmmlab.com/mmediting/dataset/singan/balloons.png) | 8 | [config](./singan_balloons.py) | [ckpt](https://download.openmmlab.com/mmediting/singan/singan_balloons_20210406_191047-8fcd94cf.pth) \| [pkl](https://download.openmmlab.com/mmediting/singan/singan_balloons_20210406_191047-8fcd94cf.pkl) | +| SinGAN | [fish.jpg](https://download.openmmlab.com/mmediting/dataset/singan/fish-crop.jpg) | 10 | [config](./singan_fish.py) | [ckpt](https://download.openmmlab.com/mmediting/singan/singan_fis_20210406_201006-860d91b6.pth) \| [pkl](https://download.openmmlab.com/mmediting/singan/singan_fis_20210406_201006-860d91b6.pkl) | +| SinGAN | [bohemian.png](https://download.openmmlab.com/mmediting/dataset/singan/bohemian.png) | 10 | [config](./singan_bohemian.py) | [ckpt](https://download.openmmlab.com/mmediting/singan/singan_bohemian_20210406_175439-f964ee38.pth) \| [pkl](https://download.openmmlab.com/mmediting/singan/singan_bohemian_20210406_175439-f964ee38.pkl) | ## Notes for using SinGAN diff --git a/configs/singan/metafile.yml b/configs/singan/metafile.yml index 20b67a3893..82ba78f349 100644 --- a/configs/singan/metafile.yml +++ b/configs/singan/metafile.yml @@ -17,7 +17,7 @@ Models: Metrics: Num Scales: 8.0 Task: Internal Learning - Weights: https://download.openmmlab.com/mmgen/singan/singan_balloons_20210406_191047-8fcd94cf.pth + Weights: https://download.openmmlab.com/mmediting/singan/singan_balloons_20210406_191047-8fcd94cf.pth - Config: configs/singan/singan_fish.py In Collection: SinGAN Metadata: @@ -28,7 +28,7 @@ Models: Metrics: Num Scales: 10.0 Task: Internal Learning - Weights: https://download.openmmlab.com/mmgen/singan/singan_fis_20210406_201006-860d91b6.pth + Weights: https://download.openmmlab.com/mmediting/singan/singan_fis_20210406_201006-860d91b6.pth - Config: configs/singan/singan_bohemian.py In Collection: SinGAN Metadata: @@ -39,4 +39,4 @@ Models: Metrics: Num Scales: 10.0 Task: Internal Learning - Weights: https://download.openmmlab.com/mmgen/singan/singan_bohemian_20210406_175439-f964ee38.pth + Weights: https://download.openmmlab.com/mmediting/singan/singan_bohemian_20210406_175439-f964ee38.pth diff --git a/configs/sngan_proj/README.md b/configs/sngan_proj/README.md index 8f3e4c0bec..adfda64dfc 100644 --- a/configs/sngan_proj/README.md +++ b/configs/sngan_proj/README.md @@ -29,14 +29,14 @@ One of the challenges in the study of generative adversarial networks is the ins | Models | Dataset | Inplace ReLU | disc_step | Total Iters\* | Iter | IS | FID | Config | Download | Log | | :--------------------------------: | :------: | :----------: | :-------: | :-----------: | :----: | :-----: | :-----: | :---------------------------------: | :-----------------------------------: | :------------------------------: | -| SNGAN_Proj-32x32-woInplaceReLU Best IS | CIFAR10 | w/o | 5 | 500000 | 400000 | 9.6919 | 9.8203 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_is-iter400000_20210709_163823-902ce1ae.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) | -| SNGAN_Proj-32x32-woInplaceReLU Best FID | CIFAR10 | w/o | 5 | 500000 | 490000 | 9.5659 | 8.1158 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) | -| SNGAN_Proj-32x32-wInplaceReLU Best IS | CIFAR10 | w | 5 | 500000 | 490000 | 9.5564 | 8.3462 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_is-iter490000_20210709_202230-cd863c74.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) | -| SNGAN_Proj-32x32-wInplaceReLU Best FID | CIFAR10 | w | 5 | 500000 | 490000 | 9.5564 | 8.3462 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) | -| SNGAN_Proj-128x128-woInplaceReLU Best IS | ImageNet | w/o | 5 | 1000000 | 952000 | 30.0651 | 33.4682 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_is-iter952000_20210730_132027-9c884a21.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_20210730_131424_fid-061bf803_is-9c884a21.json) | -| SNGAN_Proj-128x128-woInplaceReLU Best FID | ImageNet | w/o | 5 | 1000000 | 989000 | 29.5779 | 32.6193 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_20210730_131424_fid-061bf803_is-9c884a21.json) | -| SNGAN_Proj-128x128-wInplaceReLU Best IS | ImageNet | w | 5 | 1000000 | 944000 | 28.1799 | 34.3383 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_is-iter944000_20210730_132714-ca0ccd07.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_20210730_132401_fid-9a682411_is-ca0ccd07.json) | -| SNGAN_Proj-128x128-wInplaceReLU Best FID | ImageNet | w | 5 | 1000000 | 988000 | 27.7948 | 33.4821 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth) | [Log](https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_20210730_132401_fid-9a682411_is-ca0ccd07.json) | +| SNGAN_Proj-32x32-woInplaceReLU Best IS | CIFAR10 | w/o | 5 | 500000 | 400000 | 9.6919 | 9.8203 | [config](./sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_is-iter400000_20210709_163823-902ce1ae.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) | +| SNGAN_Proj-32x32-woInplaceReLU Best FID | CIFAR10 | w/o | 5 | 500000 | 490000 | 9.5659 | 8.1158 | [config](./sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_20210624_065306_fid-ba0862a0_is-902ce1ae.json) | +| SNGAN_Proj-32x32-wInplaceReLU Best IS | CIFAR10 | w | 5 | 500000 | 490000 | 9.5564 | 8.3462 | [config](./sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_is-iter490000_20210709_202230-cd863c74.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_20210624_063454_is-cd863c74_fid-191b2648.json) | +| SNGAN_Proj-32x32-wInplaceReLU Best FID | CIFAR10 | w | 5 | 500000 | 490000 | 9.5564 | 8.3462 | [config](./sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_20210624_063454_is-cd863c74_fid-191b2648.json) | +| SNGAN_Proj-128x128-woInplaceReLU Best IS | ImageNet | w/o | 5 | 1000000 | 952000 | 30.0651 | 33.4682 | [config](./sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_is-iter952000_20210730_132027-9c884a21.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_20210730_131424_fid-061bf803_is-9c884a21.json) | +| SNGAN_Proj-128x128-woInplaceReLU Best FID | ImageNet | w/o | 5 | 1000000 | 989000 | 29.5779 | 32.6193 | [config](./sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_20210730_131424_fid-061bf803_is-9c884a21.json) | +| SNGAN_Proj-128x128-wInplaceReLU Best IS | ImageNet | w | 5 | 1000000 | 944000 | 28.1799 | 34.3383 | [config](./sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_is-iter944000_20210730_132714-ca0ccd07.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_20210730_132401_fid-9a682411_is-ca0ccd07.json) | +| SNGAN_Proj-128x128-wInplaceReLU Best FID | ImageNet | w | 5 | 1000000 | 988000 | 27.7948 | 33.4821 | [config](./sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py) | [ckpt](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth) | [Log](https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_20210730_132401_fid-9a682411_is-ca0ccd07.json) | '\*' Iteration counting rule in our implementation is different from others. If you want to align with other codebases, you can use the following conversion formula: @@ -49,8 +49,8 @@ To be noted that, in Pytorch Studio GAN, **inplace ReLU** is used in generator a | Models | Dataset | Inplace ReLU | disc_step | Total Iters | IS (Our Pipeline) | FID (Our Pipeline) | IS (StudioGAN) | FID (StudioGAN) | Config | Download | Original Download link | | :-----------------: | :------: | :----------: | :-------: | :---------: | :---------------: | :----------------: | :------------: | :-------------: | :-----------------: | :--------------------: | :----------------------------------: | -| SAGAN_Proj-32x32 StudioGAN | CIFAR10 | w | 5 | 100000 | 9.372 | 10.2011 | 8.677 | 13.248 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj-cvt-studioGAN_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmgen/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth) | [model](https://drive.google.com/drive/folders/16s5Cr-V-NlfLyy_uyXEkoNxLBt-8wYSM) | -| SAGAN_Proj-128x128 StudioGAN | ImageNet | w | 2 | 1000000 | 30.218 | 29.8199 | 32.247 | 26.792 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/sngan_proj/sngan-proj-cvt-studioGAN_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmgen/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth) | [model](https://drive.google.com/drive/folders/1Ek2wAMlxpajL_M8aub4DKQ9B313K8XhS) | +| SAGAN_Proj-32x32 StudioGAN | CIFAR10 | w | 5 | 100000 | 9.372 | 10.2011 | 8.677 | 13.248 | [config](./sngan-proj-cvt-studioGAN_cifar10-32x32.py) | [model](https://download.openmmlab.com/mmediting/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth) | [model](https://drive.google.com/drive/folders/16s5Cr-V-NlfLyy_uyXEkoNxLBt-8wYSM) | +| SAGAN_Proj-128x128 StudioGAN | ImageNet | w | 2 | 1000000 | 30.218 | 29.8199 | 32.247 | 26.792 | [config](./sngan-proj-cvt-studioGAN_imagenet1k-128x128.py) | [model](https://download.openmmlab.com/mmediting/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth) | [model](https://drive.google.com/drive/folders/1Ek2wAMlxpajL_M8aub4DKQ9B313K8XhS) | - `Our Pipeline` denote results evaluated with our pipeline. - `StudioGAN` denote results released by Pytorch-StudioGAN. @@ -62,7 +62,7 @@ For IS metric, our implementation is different from PyTorch-Studio GAN in the fo For FID evaluation, we follow the pipeline of [BigGAN](https://github.com/ajbrock/BigGAN-PyTorch/blob/98459431a5d618d644d54cd1e9fceb1e5045648d/calculate_inception_moments.py#L52), where the whole training set is adopted to extract inception statistics, and Pytorch Studio GAN uses 50000 randomly selected samples. Besides, we also use [Tero's Inception](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt) for feature extraction. -You can download the preprocessed inception state by the following url: [CIFAR10](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/cifar10.pkl) and [ImageNet1k](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/imagenet.pkl). +You can download the preprocessed inception state by the following url: [CIFAR10](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/cifar10.pkl) and [ImageNet1k](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/imagenet.pkl). You can use following commands to extract those inception states by yourself. diff --git a/configs/sngan_proj/metafile.yml b/configs/sngan_proj/metafile.yml index b47f0bf097..37efed84ea 100644 --- a/configs/sngan_proj/metafile.yml +++ b/configs/sngan_proj/metafile.yml @@ -21,7 +21,7 @@ Models: Total Iters\*: 500000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_is-iter400000_20210709_163823-902ce1ae.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_is-iter400000_20210709_163823-902ce1ae.pth - Config: configs/sngan_proj/sngan-proj_woReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py In Collection: SNGAN Metadata: @@ -36,7 +36,7 @@ Models: Total Iters\*: 500000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_woReLUinplace_fid-iter490000_20210709_163329-ba0862a0.pth - Config: configs/sngan_proj/sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py In Collection: SNGAN Metadata: @@ -51,7 +51,7 @@ Models: Total Iters\*: 500000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_is-iter490000_20210709_202230-cd863c74.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4_b64x1_wReLUinplace_is-iter490000_20210709_202230-cd863c74.pth - Config: configs/sngan_proj/sngan-proj_wReLUinplace_lr2e-4-ndisc5-1xb64_cifar10-32x32.py In Collection: SNGAN Metadata: @@ -66,7 +66,7 @@ Models: Total Iters\*: 500000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_cifar10_32_lr-2e-4-b64x1_wReLUinplace_fid-iter490000_20210709_203038-191b2648.pth - Config: configs/sngan_proj/sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py In Collection: SNGAN Metadata: @@ -81,7 +81,7 @@ Models: Total Iters\*: 1000000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_is-iter952000_20210730_132027-9c884a21.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_is-iter952000_20210730_132027-9c884a21.pth - Config: configs/sngan_proj/sngan-proj_woReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py In Collection: SNGAN Metadata: @@ -96,7 +96,7 @@ Models: Total Iters\*: 1000000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_woReLUinplace_fid-iter988000_20210730_131424-061bf803.pth - Config: configs/sngan_proj/sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py In Collection: SNGAN Metadata: @@ -111,7 +111,7 @@ Models: Total Iters\*: 1000000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_is-iter944000_20210730_132714-ca0ccd07.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_is-iter944000_20210730_132714-ca0ccd07.pth - Config: configs/sngan_proj/sngan-proj_wReLUinplace_Glr2e-4_Dlr5e-5_ndisc5-2xb128_imagenet1k-128x128.py In Collection: SNGAN Metadata: @@ -126,7 +126,7 @@ Models: Total Iters\*: 1000000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_proj_imagenet1k_128_Glr2e-4_Dlr5e-5_ndisc5_b128x2_wReLUinplace_fid-iter988000_20210730_132401-9a682411.pth - Config: configs/sngan_proj/sngan-proj-cvt-studioGAN_cifar10-32x32.py In Collection: SNGAN Metadata: @@ -142,7 +142,7 @@ Models: Total Iters: 100000.0 disc_step: 5.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_cifar10_convert-studio-rgb_20210709_111346-2979202d.pth - Config: configs/sngan_proj/sngan-proj-cvt-studioGAN_imagenet1k-128x128.py In Collection: SNGAN Metadata: @@ -158,4 +158,4 @@ Models: Total Iters: 1000000.0 disc_step: 2.0 Task: Conditional GANs - Weights: https://download.openmmlab.com/mmgen/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth + Weights: https://download.openmmlab.com/mmediting/sngan_proj/sngan_imagenet1k_convert-studio-rgb_20210709_111406-877b1130.pth diff --git a/configs/srcnn/README.md b/configs/srcnn/README.md index 990c5b9c03..520fc41da6 100644 --- a/configs/srcnn/README.md +++ b/configs/srcnn/README.md @@ -25,7 +25,7 @@ The metrics are `PSNR / SSIM` . | Method | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU Info | Download | | :----------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :------: | :------------------------------------------------------------------: | -| [srcnn_x4k915_1x16_1000k_div2k](/configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py) | 28.4316 | 25.6486 | 27.7460 | 0.8099 | 0.7014 | 0.7854 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) | +| [srcnn_x4k915_1x16_1000k_div2k](./srcnn_x4k915_1xb16-1000k_div2k.py) | 28.4316 | 25.6486 | 27.7460 | 0.8099 | 0.7014 | 0.7854 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) | ## Quick Start diff --git a/configs/srcnn/README_zh-CN.md b/configs/srcnn/README_zh-CN.md index 757f2c8edc..fb5cc9e83e 100644 --- a/configs/srcnn/README_zh-CN.md +++ b/configs/srcnn/README_zh-CN.md @@ -27,9 +27,9 @@ 在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。 我们使用 `PSNR` 和 `SSIM` 作为指标。 -| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | -| :---------------------------------------------------------------------: | :--------------: | :---------------: | :--------------: | :------: | :----------------------------------------------------------------------: | -| [srcnn_x4k915_1x16_1000k_div2k](/configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py) | 28.4316 / 0.8099 | 25.6486 / 0.7014 | 27.7460 / 0.7854 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) | +| 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | +| :------------------------------------------------------------------: | :--------------: | :---------------: | :--------------: | :------: | :-------------------------------------------------------------------------: | +| [srcnn_x4k915_1x16_1000k_div2k](./srcnn_x4k915_1xb16-1000k_div2k.py) | 28.4316 / 0.8099 | 25.6486 / 0.7014 | 27.7460 / 0.7854 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608-4186f232.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/srcnn/srcnn_x4k915_1x16_1000k_div2k_20200608_120159.log.json) | ## 快速开始 diff --git a/configs/srgan_resnet/README.md b/configs/srgan_resnet/README.md index 3b51828e1f..b40bb093d3 100644 --- a/configs/srgan_resnet/README.md +++ b/configs/srgan_resnet/README.md @@ -26,8 +26,8 @@ The metrics are `PSNR / SSIM` . | Method | Set5 PSNR | Set14 PSNR | DIV2K PSNR | Set5 SSIM | Set14 SSIM | DIV2K SSIM | GPU Info | Download | | :----------------------------------------------------------------: | :-------: | :--------: | :--------: | :-------: | :--------: | :--------: | :------: | :------------------------------------------------------------------: | -| [msrresnet_x4c64b16_1x16_300k_div2k](/configs/srgan_resnet/msrresnet_x4c64b16_1xb16-1000k_div2k.py) | 30.2252 | 26.7762 | 28.9748 | 0.8491 | 0.7369 | 0.8178 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) | -| [srgan_x4c64b16_1x16_1000k_div2k](/configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py) | 27.9499 | 24.7383 | 26.5697 | 0.7846 | 0.6491 | 0.7365 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) | +| [msrresnet_x4c64b16_1x16_300k_div2k](./msrresnet_x4c64b16_1xb16-1000k_div2k.py) | 30.2252 | 26.7762 | 28.9748 | 0.8491 | 0.7369 | 0.8178 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) | +| [srgan_x4c64b16_1x16_1000k_div2k](./srgan_x4c64b16_1xb16-1000k_div2k.py) | 27.9499 | 24.7383 | 26.5697 | 0.7846 | 0.6491 | 0.7365 | 1 | [model](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) | ## Quick Start diff --git a/configs/srgan_resnet/README_zh-CN.md b/configs/srgan_resnet/README_zh-CN.md index 5d16d8db50..3758d4c1b1 100644 --- a/configs/srgan_resnet/README_zh-CN.md +++ b/configs/srgan_resnet/README_zh-CN.md @@ -25,8 +25,8 @@ | 算法 | Set5 | Set14 | DIV2K | GPU 信息 | 下载 | | :---------------------------------------------------------------------: | :---------------: | :--------------: | :--------------: | :------: | :----------------------------------------------------------------------: | -| [msrresnet_x4c64b16_1x16_300k_div2k](/configs/srgan_resnet/msrresnet_x4c64b16_1xb16-1000k_div2k.py) | 30.2252 / 0.8491 | 26.7762 / 0.7369 | 28.9748 / 0.8178 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) | -| [srgan_x4c64b16_1x16_1000k_div2k](/configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py) | 27.9499 / 0.7846 | 24.7383 / 0.6491 | 26.5697 / 0.7365 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) | +| [msrresnet_x4c64b16_1x16_300k_div2k](./msrresnet_x4c64b16_1xb16-1000k_div2k.py) | 30.2252 / 0.8491 | 26.7762 / 0.7369 | 28.9748 / 0.8178 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521-61556be5.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/msrresnet_x4c64b16_1x16_300k_div2k_20200521_110246.log.json) | +| [srgan_x4c64b16_1x16_1000k_div2k](./srgan_x4c64b16_1xb16-1000k_div2k.py) | 27.9499 / 0.7846 | 24.7383 / 0.6491 | 26.5697 / 0.7365 | 1 | [模型](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200606-a1f0810e.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/srresnet_srgan/srgan_x4c64b16_1x16_1000k_div2k_20200506_191442.log.json) | ## 快速开始 diff --git a/configs/styleganv1/README.md b/configs/styleganv1/README.md index 1df71be75d..e9f0d331f1 100644 --- a/configs/styleganv1/README.md +++ b/configs/styleganv1/README.md @@ -26,10 +26,10 @@ We propose an alternative generator architecture for generative adversarial netw -| Model | FID50k | P&R50k_full | Config | Download | -| :------------------: | :----: | :-----------: | :-----------------------------------------------------------------------------: | :--------------------------------------------------------------------------------: | -| styleganv1_ffhq_256 | 6.090 | 70.228/27.050 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv1/styleganv1_ffhq-256x256_8xb4-25Mimgs.py) | [model](https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth) | -| styleganv1_ffhq_1024 | 4.056 | 70.302/36.869 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv1/styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py) | [model](https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth) | +| Model | FID50k | P&R50k_full | Config | Download | +| :------------------: | :----: | :-----------: | :---------------------------------------------------: | :----------------------------------------------------------------------------------------------------------: | +| styleganv1_ffhq_256 | 6.090 | 70.228/27.050 | [config](./styleganv1_ffhq-256x256_8xb4-25Mimgs.py) | [model](https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth) | +| styleganv1_ffhq_1024 | 4.056 | 70.302/36.869 | [config](./styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py) | [model](https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth) | ## Citation diff --git a/configs/styleganv1/metafile.yml b/configs/styleganv1/metafile.yml index ed2d62c759..812f8aaf16 100644 --- a/configs/styleganv1/metafile.yml +++ b/configs/styleganv1/metafile.yml @@ -20,7 +20,7 @@ Models: PSNR: 70.228 SSIM: 27.05 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth + Weights: https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth - Config: configs/styleganv1/styleganv1_ffhq-1024x1024_8xb4-25Mimgs.py In Collection: StyleGANv1 Metadata: @@ -34,4 +34,4 @@ Models: PSNR: 70.302 SSIM: 36.869 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth + Weights: https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_1024_g8_25Mimg_20210407_161627-850a7234.pth diff --git a/configs/styleganv2/README.md b/configs/styleganv2/README.md index 631ea0dbb4..37a45a7d8a 100644 --- a/configs/styleganv2/README.md +++ b/configs/styleganv2/README.md @@ -28,14 +28,14 @@ The style-based GAN architecture (StyleGAN) yields state-of-the-art results in d | Model | Comment | FID50k | Precision50k | Recall50k | Config | Download | | :---------------------------------: | :-------------: | :----: | :----------: | :-------: | :----------------------------------------------------------: | :-------------------------------------------------------------: | -| stylegan2_config-f_ffhq_1024 | official weight | 2.8134 | 62.856 | 49.400 | [stylegan2_c2_8xb4_ffhq-1024x1024](/configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) | -| stylegan2_config-f_lsun-car_384x512 | official weight | 5.4316 | 65.986 | 48.190 | [stylegan2_c2_8xb4_lsun-car-384x512](/configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth) | -| stylegan2_config-f_horse_256 | official weight | - | - | - | [stylegan2_c2_8xb4-800kiters_lsun-horse-256x256](/configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth) | -| stylegan2_config-f_church_256 | official weight | - | - | - | [stylegan2_c2_8xb4-800kiters_lsun-church-256x256](/configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth) | -| stylegan2_config-f_cat_256 | official weight | - | - | - | [stylegan2_c2_8xb4-800kiters_lsun-cat-256x256](/configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth) | -| stylegan2_config-f_ffhq_256 | our training | 3.992 | 69.012 | 40.417 | [stylegan2_c2_8xb4-800kiters_ffhq-256x256](/configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) | -| stylegan2_config-f_ffhq_1024 | our training | 2.8185 | 68.236 | 49.583 | [stylegan2_c2_8xb4_ffhq-1024x1024](/configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) | -| stylegan2_config-f_lsun-car_384x512 | our training | 2.4116 | 66.760 | 50.576 | [stylegan2_c2_8xb4_lsun-car-384x512](/configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth) | +| stylegan2_config-f_ffhq_1024 | official weight | 2.8134 | 62.856 | 49.400 | [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) | +| stylegan2_config-f_lsun-car_384x512 | official weight | 5.4316 | 65.986 | 48.190 | [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun-car-384x512.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth) | +| stylegan2_config-f_horse_256 | official weight | - | - | - | [stylegan2_c2_8xb4-800kiters_lsun-horse-256x256](./stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth) | +| stylegan2_config-f_church_256 | official weight | - | - | - | [stylegan2_c2_8xb4-800kiters_lsun-church-256x256](./stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth) | +| stylegan2_config-f_cat_256 | official weight | - | - | - | [stylegan2_c2_8xb4-800kiters_lsun-cat-256x256](./stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth) | +| stylegan2_config-f_ffhq_256 | our training | 3.992 | 69.012 | 40.417 | [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) | +| stylegan2_config-f_ffhq_1024 | our training | 2.8185 | 68.236 | 49.583 | [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) | +| stylegan2_config-f_lsun-car_384x512 | our training | 2.4116 | 66.760 | 50.576 | [stylegan2_c2_8xb4_lsun-car-384x512](./stylegan2_c2_8xb4_lsun-car-384x512.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth) | ## FP16 Support and Experiments @@ -49,18 +49,18 @@ Currently, we have supported FP16 training for StyleGAN2, and here are the resul As shown in the figure, we provide **3** ways to do mixed-precision training for `StyleGAN2`: -- [stylegan2_c2_fp16_PL-no-scaler](/configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py): In this setting, we try our best to follow the official FP16 implementation in [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada). Similar to the official version, we only adopt FP16 training for the higher-resolution feature maps (the last 4 stages in G and the first 4 stages). Note that we do not adopt the `clamp` way to avoid gradient overflow used in the official implementation. We use the `autocast` function from `torch.cuda.amp` package. -- [stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler](/configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py): In this config, we try to adopt mixed-precision training for the whole generator, but in partial discriminator (the first 4 higher-resolution stages). Note that we do not apply the loss scaler in the path length loss and gradient penalty loss. Because we always meet divergence after adopting the loss scaler to scale the gradient in these two losses. -- [stylegan2_c2_apex_fp16_PL-R1-no-scaler](/configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py): In this setting, we adopt the [APEX](https://github.com/NVIDIA/apex) toolkit to implement mixed-precision training with multiple loss/gradient scalers. In APEX, you can assign different loss scalers for the generator and the discriminator respectively. Note that we still ignore the gradient scaler in the path length loss and gradient penalty loss. +- [stylegan2_c2_fp16_PL-no-scaler](./stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py): In this setting, we try our best to follow the official FP16 implementation in [StyleGAN2-ADA](https://github.com/NVlabs/stylegan2-ada). Similar to the official version, we only adopt FP16 training for the higher-resolution feature maps (the last 4 stages in G and the first 4 stages). Note that we do not adopt the `clamp` way to avoid gradient overflow used in the official implementation. We use the `autocast` function from `torch.cuda.amp` package. +- [stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler](./stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py): In this config, we try to adopt mixed-precision training for the whole generator, but in partial discriminator (the first 4 higher-resolution stages). Note that we do not apply the loss scaler in the path length loss and gradient penalty loss. Because we always meet divergence after adopting the loss scaler to scale the gradient in these two losses. +- [stylegan2_c2_apex_fp16_PL-R1-no-scaler](./stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py): In this setting, we adopt the [APEX](https://github.com/NVIDIA/apex) toolkit to implement mixed-precision training with multiple loss/gradient scalers. In APEX, you can assign different loss scalers for the generator and the discriminator respectively. Note that we still ignore the gradient scaler in the path length loss and gradient penalty loss. | Model | Comment | Dataset | FID50k | Config | Download | | :----------------------------------------------: | :-------------------------------------: | :-----: | :----: | :-----------------------------------------------: | :-------------------------------------------------: | -| stylegan2_config-f_ffhq_256 | baseline | FFHQ256 | 3.992 | [stylegan2_c2_8xb4-800kiters_ffhq-256x256](/configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) | -| stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k | partial layers in fp16 | FFHQ256 | 4.331 | [stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256](/configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth) | -| stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k | the whole G in fp16 | FFHQ256 | 4.362 | [stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256](/configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth) | -| stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k | the whole G&D in fp16 + two loss scaler | FFHQ256 | 4.614 | [stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256](/configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth) | +| stylegan2_config-f_ffhq_256 | baseline | FFHQ256 | 3.992 | [stylegan2_c2_8xb4-800kiters_ffhq-256x256](./stylegan2_c2_8xb4-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth) | +| stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k | partial layers in fp16 | FFHQ256 | 4.331 | [stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth) | +| stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k | the whole G in fp16 | FFHQ256 | 4.362 | [stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth) | +| stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k | the whole G&D in fp16 + two loss scaler | FFHQ256 | 4.614 | [stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256](./stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py) | [ckpt](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth) | -In addition, we also provide `QuickTestImageDataset` to users for quickly checking whether the code can be run correctly. It's more important for FP16 experiments, because some cuda operations may no support mixed precision training. Esepcially for `APEX`, you can use [this config](/configs/styleganv2/stylegan2_c2_8xb4-apex-fp16-800kiters_quicktest-ffhq-256x256.py) in your local machine by running: +In addition, we also provide `QuickTestImageDataset` to users for quickly checking whether the code can be run correctly. It's more important for FP16 experiments, because some cuda operations may no support mixed precision training. Esepcially for `APEX`, you can use [this config](./stylegan2_c2_8xb4-apex-fp16-800kiters_quicktest-ffhq-256x256.py) in your local machine by running: ```bash bash tools/dist_train.sh \ @@ -68,7 +68,7 @@ bash tools/dist_train.sh \ --work-dir ./work_dirs/quick-test ``` -With a similar way, users can switch to [config for partial-GD](/configs/styleganv2/stylegan2_c2_8xb4-fp16-800kiters_quicktest-ffhq-256x256.py) and [config for globalG-partialD](/configs/styleganv2/stylegan2_c2_8xb4-fp16-global-800kiters_quicktest-ffhq-256x256.py) to test the other two mixed precision training configuration. +With a similar way, users can switch to [config for partial-GD](./stylegan2_c2_8xb4-fp16-800kiters_quicktest-ffhq-256x256.py) and [config for globalG-partialD](./stylegan2_c2_8xb4-fp16-global-800kiters_quicktest-ffhq-256x256.py) to test the other two mixed precision training configuration. *Note that to use the [APEX](https://github.com/NVIDIA/apex) toolkit, you have to installed it following the official guidance. (APEX is not included in our requirements.) If you are using GPUs without tensor core, you would better to switch to the newer PyTorch version (>= 1.7,0). Otherwise, the APEX installation or running may meet several bugs.* @@ -76,17 +76,17 @@ With a similar way, users can switch to [config for partial-GD](/configs/stylega | Model | Comment | FID50k | FID Version | Config | Download | | :--------------------------: | :-------------: | :----: | :-------------: | :-----------------------------------------------------------------: | :-------------------------------------------------------------------: | -| stylegan2_config-f_ffhq_1024 | official weight | 2.8732 | Tero's StyleGAN | [stylegan2_c2_8xb4_ffhq-1024x1024](/configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) \| [FID-Reals](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/ffhq-1024-50k-stylegan.pkl) | -| stylegan2_config-f_ffhq_1024 | our training | 2.9413 | Tero's StyleGAN | [stylegan2_c2_8xb4_ffhq-1024x1024](/configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) \| [FID-Reals](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/ffhq-1024-50k-stylegan.pkl) | -| stylegan2_config-f_ffhq_1024 | official weight | 2.8134 | Our PyTorch | [stylegan2_c2_8xb4_ffhq-1024x1024](/configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) \| [FID-Reals](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/ffhq-1024-50k-rgb.pkl) | -| stylegan2_config-f_ffhq_1024 | our training | 2.8185 | Our PyTorch | [stylegan2_c2_8xb4_ffhq-1024x1024](/configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) \| [FID-Reals](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/ffhq-1024-50k-rgb.pkl) | +| stylegan2_config-f_ffhq_1024 | official weight | 2.8732 | Tero's StyleGAN | [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) \| [FID-Reals](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/ffhq-1024-50k-stylegan.pkl) | +| stylegan2_config-f_ffhq_1024 | our training | 2.9413 | Tero's StyleGAN | [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) \| [FID-Reals](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/ffhq-1024-50k-stylegan.pkl) | +| stylegan2_config-f_ffhq_1024 | official weight | 2.8134 | Our PyTorch | [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth) \| [FID-Reals](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/ffhq-1024-50k-rgb.pkl) | +| stylegan2_config-f_ffhq_1024 | our training | 2.8185 | Our PyTorch | [stylegan2_c2_8xb4_ffhq-1024x1024](./stylegan2_c2_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth) \| [FID-Reals](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/ffhq-1024-50k-rgb.pkl) | In this table, we observe that the FID with Tero's inception network is similar to that with PyTorch Inception (in mmediting). Thus, we use the FID with PyTorch's Inception net (but the weight is not the official model zoo) by default. Because it can be run on different PyTorch versions. If you use [Tero's Inception net](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt), your PyTorch must meet `>=1.6.0`. More precalculated inception pickle files are listed here: -- FFHQ 256x256 real inceptions, PyTorch InceptionV3. [download](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/ffhq-256-50k-rgb.pkl) -- LSUN-Car 384x512 real inceptions, PyTorch InceptionV3. [download](https://download.openmmlab.com/mmgen/evaluation/fid_inception_pkl/lsun-car-512_50k_rgb.pkl) +- FFHQ 256x256 real inceptions, PyTorch InceptionV3. [download](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/ffhq-256-50k-rgb.pkl) +- LSUN-Car 384x512 real inceptions, PyTorch InceptionV3. [download](https://download.openmmlab.com/mmediting/evaluation/fid_inception_pkl/lsun-car-512_50k_rgb.pkl) ## About Different Implementation and Setting of PR Metric diff --git a/configs/styleganv2/metafile.yml b/configs/styleganv2/metafile.yml index e8c2a5f8bf..88d6b0e23e 100644 --- a/configs/styleganv2/metafile.yml +++ b/configs/styleganv2/metafile.yml @@ -19,7 +19,7 @@ Models: Precision50k: 62.856 Recall50k: 49.4 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py In Collection: StyleGANv2 Metadata: @@ -32,7 +32,7 @@ Models: Precision50k: 65.986 Recall50k: 48.19 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth - Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-horse-256x256.py In Collection: StyleGANv2 Metadata: @@ -42,7 +42,7 @@ Models: - Dataset: Others Metrics: {} Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth - Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py In Collection: StyleGANv2 Metadata: @@ -52,7 +52,7 @@ Models: - Dataset: Others Metrics: {} Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth - Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-cat-256x256.py In Collection: StyleGANv2 Metadata: @@ -62,7 +62,7 @@ Models: - Dataset: CAT Metrics: {} Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth - Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py In Collection: StyleGANv2 Metadata: @@ -75,7 +75,7 @@ Models: Precision50k: 69.012 Recall50k: 40.417 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py In Collection: StyleGANv2 Metadata: @@ -88,7 +88,7 @@ Models: Precision50k: 68.236 Recall50k: 49.583 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_lsun-car-384x512.py In Collection: StyleGANv2 Metadata: @@ -101,7 +101,7 @@ Models: Precision50k: 66.76 Recall50k: 50.576 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_lsun-car_384x512_b4x8_1800k_20210424_160929-fc9072ca.pth - Config: configs/styleganv2/stylegan2_c2_8xb4-800kiters_ffhq-256x256.py In Collection: StyleGANv2 Metadata: @@ -112,7 +112,7 @@ Models: Metrics: FID50k: 3.992 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_256_b4x8_20210407_160709-7890ae1f.pth - Config: configs/styleganv2/stylegan2_c2-PL_8xb4-fp16-partial-GD-no-scaler-800kiters_ffhq-256x256.py In Collection: StyleGANv2 Metadata: @@ -123,7 +123,7 @@ Models: Metrics: FID50k: 4.331 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16_partial-GD_PL-no-scaler_ffhq_256_b4x8_800k_20210508_114854-dacbe4c9.pth - Config: configs/styleganv2/stylegan2_c2-PL-R1_8xb4-fp16-globalG-partialD-no-scaler-800kiters_ffhq-256x256.py In Collection: StyleGANv2 Metadata: @@ -134,7 +134,7 @@ Models: Metrics: FID50k: 4.362 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_fp16-globalG-partialD_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114930-ef8270d4.pth - Config: configs/styleganv2/stylegan2_c2-PL-R1_8xb4-apex-fp16-no-scaler-800kiters_ffhq-256x256.py In Collection: StyleGANv2 Metadata: @@ -145,7 +145,7 @@ Models: Metrics: FID50k: 4.614 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_apex_fp16_PL-R1-no-scaler_ffhq_256_b4x8_800k_20210508_114701-c2bb8afd.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py In Collection: StyleGANv2 Metadata: @@ -156,7 +156,7 @@ Models: Metrics: FID50k: 2.8732 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py In Collection: StyleGANv2 Metadata: @@ -167,7 +167,7 @@ Models: Metrics: FID50k: 2.9413 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py In Collection: StyleGANv2 Metadata: @@ -178,7 +178,7 @@ Models: Metrics: FID50k: 2.8134 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth - Config: configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py In Collection: StyleGANv2 Metadata: @@ -189,4 +189,4 @@ Models: Metrics: FID50k: 2.8185 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth + Weights: https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth diff --git a/configs/styleganv3/README.md b/configs/styleganv3/README.md index bcb1c677d4..7c67da280c 100644 --- a/configs/styleganv3/README.md +++ b/configs/styleganv3/README.md @@ -40,26 +40,26 @@ For user convenience, we also offer the converted version of official weights. | Model | Dataset | Iter | FID50k | Config | Log | Download | | :-------------: | :---------------: | :----: | :---------------: | :----------------------------------------------: | :--------------------------------------------: | :-------------------------------------------------: | -| stylegan3-t | ffhq 1024x1024 | 490000 | 3.37\* | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024.py) | [log](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_20220322_090417.log.json) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_best_fid_iter_490000_20220401_120733-4ff83434.pth) | -| stylegan3-t-ada | metface 1024x1024 | 130000 | 15.09 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py) | [log](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_20220328_142211.log.json) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth) | +| stylegan3-t | ffhq 1024x1024 | 490000 | 3.37\* | [config](./stylegan3-t_gamma32.8_8xb4-fp16-noaug_ffhq-1024x1024.py) | [log](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_20220322_090417.log.json) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_best_fid_iter_490000_20220401_120733-4ff83434.pth) | +| stylegan3-t-ada | metface 1024x1024 | 130000 | 15.09 | [config](./stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py) | [log](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_20220328_142211.log.json) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth) | ### Experimental Settings | Model | Dataset | Iter | FID50k | Config | Log | Download | | :-------------: | :------------: | :----: | :----: | :---------------------------------------------------: | :------------------------------------------------: | :------------------------------------------------------: | -| stylegan3-t | ffhq 256x256 | 740000 | 4.51 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py) | [log](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_20220323_144815.log.json) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_best_fid_iter_740000_20220401_122456-730e1fba.pth) | -| stylegan3-r-ada | ffhq 1024x1024 | - | - | [config](/configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py) | - | - | +| stylegan3-t | ffhq 256x256 | 740000 | 4.51 | [config](./stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py) | [log](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_20220323_144815.log.json) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_best_fid_iter_740000_20220401_122456-730e1fba.pth) | +| stylegan3-r-ada | ffhq 1024x1024 | - | - | [config](./stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py) | - | - | ### Converted Weights -| Model | Dataset | Comment | FID50k | EQ-T | EQ-R | Config | Download | -| :---------: | :------------: | :-------------: | :----: | :---: | :---: | :---------------------------------------------------------------------: | :-----------------------------------------------------------------------: | -| stylegan3-t | ffhqu 256x256 | official weight | 4.62 | 63.01 | 13.12 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256.py) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ffhqu_256_b4x8_cvt_official_rgb_20220329_235046-153df4c8.pth) | -| stylegan3-t | afhqv2 512x512 | official weight | 4.04 | 60.15 | 13.51 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official_rgb_20220329_235017-ee6b037a.pth) | -| stylegan3-t | ffhq 1024x1024 | official weight | 2.79 | 61.21 | 13.82 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth) | -| stylegan3-r | ffhqu 256x256 | official weight | 4.50 | 66.65 | 40.48 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official_rgb_20220329_234909-4521d963.pth) | -| stylegan3-r | afhqv2 512x512 | official weight | 4.40 | 64.89 | 40.34 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_afhqv2_512_b4x8_cvt_official_rgb_20220329_234829-f2eaca72.pth) | -| stylegan3-r | ffhq 1024x1024 | official weight | 3.07 | 64.76 | 46.62 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth) | +| Model | Dataset | Comment | FID50k | EQ-T | EQ-R | Config | Download | +| :---------: | :------------: | :-------------: | :----: | :---: | :---: | :---------------------------------------------------------------: | :-----------------------------------------------------------------------------: | +| stylegan3-t | ffhqu 256x256 | official weight | 4.62 | 63.01 | 13.12 | [config](./stylegan3-t_cvt-official-rgb_8xb4_ffhqu-256x256.py) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhqu_256_b4x8_cvt_official_rgb_20220329_235046-153df4c8.pth) | +| stylegan3-t | afhqv2 512x512 | official weight | 4.04 | 60.15 | 13.51 | [config](./stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official_rgb_20220329_235017-ee6b037a.pth) | +| stylegan3-t | ffhq 1024x1024 | official weight | 2.79 | 61.21 | 13.82 | [config](./stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth) | +| stylegan3-r | ffhqu 256x256 | official weight | 4.50 | 66.65 | 40.48 | [config](./stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official_rgb_20220329_234909-4521d963.pth) | +| stylegan3-r | afhqv2 512x512 | official weight | 4.40 | 64.89 | 40.34 | [config](./stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_afhqv2_512_b4x8_cvt_official_rgb_20220329_234829-f2eaca72.pth) | +| stylegan3-r | ffhq 1024x1024 | official weight | 3.07 | 64.76 | 46.62 | [config](./stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py) | [model](https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth) | ## Interpolation @@ -67,7 +67,7 @@ We provide a tool to generate video by walking through GAN's latent space. Run this command to get the following video. ```bash -python apps/interpolate_sample.py configs/styleganv3/stylegan3_t_afhqv2_512_b4x8_official.py https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official.pkl --export-video --samples-path work_dirs/demos/ --endpoint 6 --interval 60 --space z --seed 2022 --sample-cfg truncation=0.8 +python apps/interpolate_sample.py configs/styleganv3/stylegan3_t_afhqv2_512_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official.pkl --export-video --samples-path work_dirs/demos/ --endpoint 6 --interval 60 --space z --seed 2022 --sample-cfg truncation=0.8 ``` https://user-images.githubusercontent.com/22982797/151506918-83da9ee3-0d63-4c5b-ad53-a41562b92075.mp4 @@ -78,11 +78,11 @@ We also provide a tool to visualize the equivarience properties for StyleGAN3. Run these commands to get the results below. ```bash -python tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.5 --transform rotate --seed 5432 +python tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.5 --transform rotate --seed 5432 -python tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmgen/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.25 --transform x_t --seed 5432 +python tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.25 --transform x_t --seed 5432 -python tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://openmmlab-share.oss-cn-hangzhou.aliyuncs.com/mmgen/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.25 --transform y_t --seed 5432 +python tools/utils/equivariance_viz.py configs/styleganv3/stylegan3_r_ffhqu_256_b4x8_official.py https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official.pkl --translate_max 0.25 --transform y_t --seed 5432 ``` https://user-images.githubusercontent.com/22982797/151504902-f3cbfef5-9014-4607-bbe1-deaf48ec6d55.mp4 @@ -102,7 +102,7 @@ metrics = dict( compute_eqt_int=True, compute_eqt_frac=True, compute_eqr=True))) ``` -And we highly recommend you to use [slurm_eval_multi_gpu](tools/slurm_eval_multi_gpu.sh) script to accelerate evaluation time. +And we highly recommend you to use [slurm_test.sh](../../tools/slurm_test.sh) script to accelerate evaluation time. ## Citation diff --git a/configs/styleganv3/metafile.yml b/configs/styleganv3/metafile.yml index e2a27ca177..97a2d263df 100644 --- a/configs/styleganv3/metafile.yml +++ b/configs/styleganv3/metafile.yml @@ -17,7 +17,7 @@ Models: Metrics: Iter: 490000.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_best_fid_iter_490000_20220401_120733-4ff83434.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma32.8_ffhq_1024_b4x8_best_fid_iter_490000_20220401_120733-4ff83434.pth - Config: configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py In Collection: StyleGANv3 Metadata: @@ -29,7 +29,7 @@ Models: FID50k: 15.09 Iter: 130000.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ada_fp16_gamma6.6_metfaces_1024_b4x8_best_fid_iter_130000_20220401_115101-f2ef498e.pth - Config: configs/styleganv3/stylegan3-t_gamma2.0_8xb4-fp16-noaug_ffhq-256x256.py In Collection: StyleGANv3 Metadata: @@ -41,7 +41,7 @@ Models: FID50k: 4.51 Iter: 740000.0 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_best_fid_iter_740000_20220401_122456-730e1fba.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_noaug_fp16_gamma2.0_ffhq_256_b4x8_best_fid_iter_740000_20220401_122456-730e1fba.pth - Config: configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py In Collection: StyleGANv3 Metadata: @@ -64,7 +64,7 @@ Models: EQ-T: 63.01 FID50k: 4.62 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ffhqu_256_b4x8_cvt_official_rgb_20220329_235046-153df4c8.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhqu_256_b4x8_cvt_official_rgb_20220329_235046-153df4c8.pth - Config: configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_afhqv2-512x512.py In Collection: StyleGANv3 Metadata: @@ -77,7 +77,7 @@ Models: EQ-T: 60.15 FID50k: 4.04 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official_rgb_20220329_235017-ee6b037a.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_afhqv2_512_b4x8_cvt_official_rgb_20220329_235017-ee6b037a.pth - Config: configs/styleganv3/stylegan3-t_cvt-official-rgb_8xb4_ffhq-1024x1024.py In Collection: StyleGANv3 Metadata: @@ -90,7 +90,7 @@ Models: EQ-T: 61.21 FID50k: 2.79 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth - Config: configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhqu-256x256.py In Collection: StyleGANv3 Metadata: @@ -103,7 +103,7 @@ Models: EQ-T: 66.65 FID50k: 4.5 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official_rgb_20220329_234909-4521d963.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhqu_256_b4x8_cvt_official_rgb_20220329_234909-4521d963.pth - Config: configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4x8_afhqv2-512x512.py In Collection: StyleGANv3 Metadata: @@ -116,7 +116,7 @@ Models: EQ-T: 64.89 FID50k: 4.4 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_afhqv2_512_b4x8_cvt_official_rgb_20220329_234829-f2eaca72.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_afhqv2_512_b4x8_cvt_official_rgb_20220329_234829-f2eaca72.pth - Config: configs/styleganv3/stylegan3-r_cvt-official-rgb_8xb4_ffhq-1024x1024.py In Collection: StyleGANv3 Metadata: @@ -129,4 +129,4 @@ Models: EQ-T: 64.76 FID50k: 3.07 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth + Weights: https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth diff --git a/configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py b/configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py index cef1bab104..094fa6edae 100644 --- a/configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py +++ b/configs/styleganv3/stylegan3-r_ada-gamma3.3_8xb4-fp16_metfaces-1024x1024.py @@ -19,7 +19,7 @@ g_reg_ratio = g_reg_interval / (g_reg_interval + 1) d_reg_ratio = d_reg_interval / (d_reg_interval + 1) -load_from = 'https://download.openmmlab.com/mmgen/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth' # noqa +load_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_r_ffhq_1024_b4x8_cvt_official_rgb_20220329_234933-ac0500a1.pth' # noqa # ada settings aug_kwargs = { diff --git a/configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py b/configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py index eb62691618..56ced90640 100644 --- a/configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py +++ b/configs/styleganv3/stylegan3-t_ada-gamma6.6_8xb4-fp16_metfaces-1024x1024.py @@ -17,7 +17,7 @@ g_reg_ratio = g_reg_interval / (g_reg_interval + 1) d_reg_ratio = d_reg_interval / (d_reg_interval + 1) -load_from = 'https://download.openmmlab.com/mmgen/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth' # noqa +load_from = 'https://download.openmmlab.com/mmediting/stylegan3/stylegan3_t_ffhq_1024_b4x8_cvt_official_rgb_20220329_235113-db6c6580.pth' # noqa # ada settings aug_kwargs = { 'xflip': 1, diff --git a/configs/tdan/README.md b/configs/tdan/README.md index 9849acacee..21a846bfa3 100644 --- a/configs/tdan/README.md +++ b/configs/tdan/README.md @@ -25,17 +25,17 @@ The metrics are `PSNR / SSIM` . | Method | Vid4 (BIx4) PSNR (Y) | SPMCS-30 (BIx4) PSNR (Y) | Vid4 (BDx4) PSNR (Y) | SPMCS-30 (BDx4) PSNR (Y) | GPU Info | Download | | :-----------------------------------------: | :------------------: | :----------------------: | :------------------: | :----------------------: | :----------------------: | :-------------------------------------------: | -| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](/configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | -| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](/configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | -| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](/configs/tdan/tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **26.49** | **30.42** | 25.93 | 29.69 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) | -| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](/configs/tdan/tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 25.80 | 29.56 | **26.87** | **30.77** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) | +| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | +| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | +| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **26.49** | **30.42** | 25.93 | 29.69 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) | +| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 25.80 | 29.56 | **26.87** | **30.77** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) | | Method | Vid4 (BIx4) SSIM (Y) | SPMCS-30 (BIx4) SSIM (Y) | Vid4 (BDx4) SSIM (Y) | SPMCS-30 (BDx4) SSIM (Y) | GPU Info | Download | | :-----------------------------------------: | :------------------: | :----------------------: | :------------------: | :----------------------: | :----------------------: | :-------------------------------------------: | -| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](/configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | -| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](/configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | -| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](/configs/tdan/tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **0.792** | **0.856** | 0.772 | 0.842 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) | -| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](/configs/tdan/tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 0.784 | 0.851 | **0.815** | **0.868** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) | +| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | +| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | +| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **0.792** | **0.856** | 0.772 | 0.842 | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) | +| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 0.784 | 0.851 | **0.815** | **0.868** | 8 (Tesla V100-SXM2-32GB) | [model](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) | ## Quick Start diff --git a/configs/tdan/README_zh-CN.md b/configs/tdan/README_zh-CN.md index 8b144620fa..e960d82e15 100644 --- a/configs/tdan/README_zh-CN.md +++ b/configs/tdan/README_zh-CN.md @@ -25,10 +25,10 @@ | 算法 | Vid4 (BIx4) | SPMCS-30 (BIx4) | Vid4 (BDx4) | SPMCS-30 (BDx4) | GPU 信息 | 下载 | | :--------------------------------------------------------: | :-------------: | :-------------: | :-------------: | :-------------: | :----------------------: | :--------------------------------------------------------: | -| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](/configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | -| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](/configs/tdan/tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | -| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](/configs/tdan/tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **26.49/0.792** | **30.42/0.856** | 25.93/0.772 | 29.69/0.842 | 8 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) | -| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](/configs/tdan/tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 25.80/0.784 | 29.56/0.851 | **26.87/0.815** | **30.77/0.868** | 8 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) | +| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bi](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bi.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | +| [tdan_x4_1xb16-lr1e-4-400k_vimeo90k-bd](./tdan_x4_8xb16-lr1e-4-400k_vimeo90k-bd.py) | - | - | - | - | 8 (Tesla V100-SXM2-32GB) | - | +| [tdan_x4ft_1xb16-lr5e-5-400k_vimeo90k-bi](./tdan_x4ft_8xb16-lr5e-5-400k_vimeo90k-bi.py) | **26.49/0.792** | **30.42/0.856** | 25.93/0.772 | 29.69/0.842 | 8 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528-739979d9.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bix4_20210528_135616.log.json) | +| [tdan_x4ft_1xb16-lr5e-5-800k_vimeo90k-bd](./tdan_x4ft_8xb16-lr5e-5-800k_vimeo90k-bd.py) | 25.80/0.784 | 29.56/0.851 | **26.87/0.815** | **30.77/0.868** | 8 (Tesla V100-SXM2-32GB) | [模型](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528-c53ab844.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/tdan/tdan_vimeo90k_bdx4_20210528_122401.log.json) | ## 快速开始 diff --git a/configs/tof/README.md b/configs/tof/README.md index f623808251..aecf07bae2 100644 --- a/configs/tof/README.md +++ b/configs/tof/README.md @@ -25,28 +25,28 @@ The metrics are `PSNR / SSIM` . | Method | Pretrained SPyNet | PSNR | GPU Info | Download | | :---------------------------------------------------: | :---------------------------------------------------------------: | :-----: | :-----------------: | :------------------------------------------------------: | -| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 33.3294 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) | -| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 33.3339 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) | -| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 33.3170 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) | -| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 33.3237 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) | -| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 33.3426 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) | +| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](./tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 33.3294 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) | +| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](./tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 33.3339 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) | +| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 33.3170 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) | +| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 33.3237 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) | +| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](./tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 33.3426 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) | | Method | Pretrained SPyNet | SSIM | GPU Info | Download | | :----------------------------------------------------: | :---------------------------------------------------------------: | :----: | :-----------------: | :------------------------------------------------------: | -| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 0.9465 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) | -| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 0.9466 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) | -| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 0.9464 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) | -| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 0.9465 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) | -| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 0.9467 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) | +| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](./tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 0.9465 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) | +| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](./tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 0.9466 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) | +| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 0.9464 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) | +| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 0.9465 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) | +| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](./tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 0.9467 | 1 (Tesla PG503-216) | [model](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \| [log](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) | Note: These pretrained SPyNets don't contain BN layer since `batch_size=1`, which is consistent with `https://github.com/Coldog2333/pytoflow`. Evaluated on RGB channels. The metrics are `PSNR / SSIM` . -| Method | Vid4 | GPU Info | Download | -| :------------------------------------------------------------------: | :--------------: | :------: | :---------------------------------------------------------------------------------------------------: | -| [tof_x4_vimeo90k_official](/configs/tof/tof_x4_official_vimeo90k.py) | 24.4377 / 0.7433 | - | [model](https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth) | +| Method | Vid4 | GPU Info | Download | +| :-------------------------------------------------------: | :--------------: | :------: | :---------------------------------------------------------------------------------------------------: | +| [tof_x4_vimeo90k_official](./tof_x4_official_vimeo90k.py) | 24.4377 / 0.7433 | - | [model](https://download.openmmlab.com/mmediting/restorers/tof/tof_x4_vimeo90k_official-a569ff50.pth) | ## Quick Start diff --git a/configs/tof/README_zh-CN.md b/configs/tof/README_zh-CN.md index 6901e9c8b3..86d04b0793 100644 --- a/configs/tof/README_zh-CN.md +++ b/configs/tof/README_zh-CN.md @@ -13,11 +13,11 @@ | 算法 | 预训练 SPyNet | Vimeo90k-triplet | GPU 信息 | 下载 | | :--------------------------------------------------: | :----------------------------------------------------------: | :--------------: | :-----------------: | :---------------------------------------------------: | -| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 33.3294 / 0.9465 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) | -| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 33.3339 / 0.9466 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) | -| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 33.3170 / 0.9464 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) | -| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 33.3237 / 0.9465 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) | -| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](/configs/tof/tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 33.3426 / 0.9467 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) | +| [tof_vfi_spynet_chair_nobn_1xb1_vimeo90k](./tof_spynet-chair-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_chair_20220321-4d82e91b.pth) | 33.3294 / 0.9465 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_chair_nobn_1xb1_vimeo90k_20220321-2fc9e258.log.json) | +| [tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k](./tof_spynet-kitti-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_kitti_20220321-dbcc1cc1.pth) | 33.3339 / 0.9466 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_kitti_nobn_1xb1_vimeo90k_20220321-3f7ca4cd.log.json) | +| [tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-clean_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_clean_20220321-0756630b.pth) | 33.3170 / 0.9464 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_clean_nobn_1xb1_vimeo90k_20220321-6e52a6fd.log.json) | +| [tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k](./tof_spynet-sintel-wobn-final_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_sintel_final_20220321-5e89dcec.pth) | 33.3237 / 0.9465 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_sintel_final_nobn_1xb1_vimeo90k_20220321-8ab70dbb.log.json) | +| [tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k](./tof_spynet-pytoflow-wobn_1xb1_vimeo90k-triplet.py) | [spynet_chairs_final](https://download.openmmlab.com/mmediting/video_interpolators/toflow/pretrained_spynet_pytoflow_20220321-5bab842d.pth) | 33.3426 / 0.9467 | 1 (Tesla PG503-216) | [模型](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.pth) \| [日志](https://download.openmmlab.com/mmediting/video_interpolators/toflow/tof_vfi_spynet_pytoflow_nobn_1xb1_vimeo90k_20220321-5f4b243e.log.json) | 注: 由于 `batch_size=1` 预训练的 SPyNet 不包含 BN 层，这与 `https://github.com/Coldog2333/pytoflow` 一致. diff --git a/configs/ttsr/README.md b/configs/ttsr/README.md index bf9c63bd8f..bba437c4ef 100644 --- a/configs/ttsr/README.md +++ b/configs/ttsr/README.md @@ -23,10 +23,10 @@ We study on image super-resolution (SR), which aims to recover realistic texture Evaluated on CUFED dataset (RGB channels), `scale` pixels in each border are cropped before evaluation. The metrics are `PSNR and SSIM` . -| Method | scale | PSNR | SSIM | GPU Info | Download | -| :----------------------------------------------------------------------------------: | :---: | :-----: | :----: | :----------: | :------------------------------------------------------------------------------------: | -| [ttsr-rec_x4_c64b16_g1_200k_CUFED](/configs/ttsr/ttsr-rec_x4c64b16_1xb9-200k_CUFED.py) | x4 | 25.2433 | 0.7491 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) | -| [ttsr-gan_x4_c64b16_g1_500k_CUFED](/configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py) | x4 | 24.6075 | 0.7234 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) | +| Method | scale | PSNR | SSIM | GPU Info | Download | +| :------------------------------------------------------------------------: | :---: | :-----: | :----: | :----------: | :----------------------------------------------------------------------------------------------: | +| [ttsr-rec_x4_c64b16_g1_200k_CUFED](./ttsr-rec_x4c64b16_1xb9-200k_CUFED.py) | x4 | 25.2433 | 0.7491 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) | +| [ttsr-gan_x4_c64b16_g1_500k_CUFED](./ttsr-gan_x4c64b16_1xb9-500k_CUFED.py) | x4 | 24.6075 | 0.7234 | 1 (TITAN Xp) | [model](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) | ## Quick Start diff --git a/configs/ttsr/README_zh-CN.md b/configs/ttsr/README_zh-CN.md index 18007679c0..3f8de2dc35 100644 --- a/configs/ttsr/README_zh-CN.md +++ b/configs/ttsr/README_zh-CN.md @@ -24,10 +24,10 @@ 在 RGB 通道上进行评估，在评估之前裁剪每个边界中的 `scale` 像素。 我们使用 `PSNR` 和 `SSIM` 作为指标。 -| 算法 | scale | CUFED | GPU 信息 | 下载 | -| :---------------------------------------------------------------------------------: | :---: | :--------------: | :----------: | :----------------------------------------------------------------------------------: | -| [ttsr-rec_x4_c64b16_g1_200k_CUFED](/configs/ttsr/ttsr-rec_x4c64b16_1xb9-200k_CUFED.py) | x4 | 25.2433 / 0.7491 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) | -| [ttsr-gan_x4_c64b16_g1_500k_CUFED](/configs/ttsr/ttsr-gan_x4c64b16_1xb9-500k_CUFED.py) | x4 | 24.6075 / 0.7234 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) | +| 算法 | scale | CUFED | GPU 信息 | 下载 | +| :------------------------------------------------------------------------: | :---: | :--------------: | :----------: | :-------------------------------------------------------------------------------------------: | +| [ttsr-rec_x4_c64b16_g1_200k_CUFED](./ttsr-rec_x4c64b16_1xb9-200k_CUFED.py) | x4 | 25.2433 / 0.7491 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-rec_x4_c64b16_g1_200k_CUFED_20210525-b0dba584.log.json) | +| [ttsr-gan_x4_c64b16_g1_500k_CUFED](./ttsr-gan_x4c64b16_1xb9-500k_CUFED.py) | x4 | 24.6075 / 0.7234 | 1 (TITAN Xp) | [模型](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.pth) \| [日志](https://download.openmmlab.com/mmediting/restorers/ttsr/ttsr-gan_x4_c64b16_g1_500k_CUFED_20210626-2ab28ca0.log.json) | ## 快速开始 diff --git a/configs/wgan-gp/README.md b/configs/wgan-gp/README.md index b88d74875c..9d9ae9c436 100644 --- a/configs/wgan-gp/README.md +++ b/configs/wgan-gp/README.md @@ -28,8 +28,8 @@ Generative Adversarial Networks (GANs) are powerful generative models, but suffe | Models | Dataset | Details | SWD | MS-SSIM | Config | Download | | :---------: | :------------: | :----------------: | :---------------------------: | :-----: | :---------------------------------------------------------: | :------------------------------------------------------------: | -| WGAN-GP 128 | CelebA-Cropped | GN | 5.87, 9.76, 9.43, 18.84/10.97 | 0.2601 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/wgan-gp/wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmgen/wgangp/wgangp_GN_celeba-cropped_128_b64x1_160k_20210408_170611-f8a99336.pth) | -| WGAN-GP 128 | LSUN-Bedroom | GN, GP-lambda = 50 | 11.7, 7.87, 9.82, 25.36/13.69 | 0.059 | [config](https://github.com/open-mmlab/mmediting/tree/master/configs/wgan-gp/wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py) | [model](https://download.openmmlab.com/mmgen/wgangp/wgangp_GN_GP-50_lsun-bedroom_128_b64x1_130k_20210408_170509-56f2a37c.pth) | +| WGAN-GP 128 | CelebA-Cropped | GN | 5.87, 9.76, 9.43, 18.84/10.97 | 0.2601 | [config](./wgangp_GN_1xb64-160kiters_celeba-cropped-128x128.py) | [model](https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_celeba-cropped_128_b64x1_160k_20210408_170611-f8a99336.pth) | +| WGAN-GP 128 | LSUN-Bedroom | GN, GP-lambda = 50 | 11.7, 7.87, 9.82, 25.36/13.69 | 0.059 | [config](./wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py) | [model](https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_GP-50_lsun-bedroom_128_b64x1_130k_20210408_170509-56f2a37c.pth) | ## Citation diff --git a/configs/wgan-gp/metafile.yml b/configs/wgan-gp/metafile.yml index 06bdc773f0..54979c2263 100644 --- a/configs/wgan-gp/metafile.yml +++ b/configs/wgan-gp/metafile.yml @@ -17,7 +17,7 @@ Models: Metrics: MS-SSIM: 0.2601 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/wgangp/wgangp_GN_celeba-cropped_128_b64x1_160k_20210408_170611-f8a99336.pth + Weights: https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_celeba-cropped_128_b64x1_160k_20210408_170611-f8a99336.pth - Config: configs/wgan-gp/wgangp_GN-GP-50_1xb64-160kiters_lsun-bedroom-128x128.py In Collection: WGAN-GP Metadata: @@ -28,4 +28,4 @@ Models: Metrics: MS-SSIM: 0.059 Task: Unconditional GANs - Weights: https://download.openmmlab.com/mmgen/wgangp/wgangp_GN_GP-50_lsun-bedroom_128_b64x1_130k_20210408_170509-56f2a37c.pth + Weights: https://download.openmmlab.com/mmediting/wgangp/wgangp_GN_GP-50_lsun-bedroom_128_b64x1_130k_20210408_170509-56f2a37c.pth diff --git a/demo/mmediting_inference_tutorial.ipynb b/demo/mmediting_inference_tutorial.ipynb index 3fe752fcc2..96c2fdfee4 100644 --- a/demo/mmediting_inference_tutorial.ipynb +++ b/demo/mmediting_inference_tutorial.ipynb @@ -480,7 +480,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\n" + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\n" ] } ], @@ -547,7 +547,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n" + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n" ] }, { @@ -597,7 +597,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n" + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n" ] }, { @@ -643,7 +643,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n", + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n", "['num_batches', 'sample_model']\n" ] } @@ -691,7 +691,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n" + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/biggan/biggan_imagenet1k_128x128_b32x8_best_fid_iter_1232000_20211111_122548-5315b13d.pth\n" ] }, { @@ -968,7 +968,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\n" + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_1x1_80k_facades_20210902_170442-c0958d50.pth\n" ] }, { @@ -1023,7 +1023,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "http loads checkpoint from path: https://download.openmmlab.com/mmgen/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth\n", + "http loads checkpoint from path: https://download.openmmlab.com/mmediting/styleganv1/styleganv1_ffhq_256_g8_25Mimg_20210407_161748-0094da86.pth\n", "Switch to evaluation style mode: single\n", "Switch to evaluation style mode: single\n" ] diff --git a/docs/en/get_started/quick_run.md b/docs/en/get_started/quick_run.md index cddaacbf4d..b243e5263a 100644 --- a/docs/en/get_started/quick_run.md +++ b/docs/en/get_started/quick_run.md @@ -9,7 +9,7 @@ from mmedit.apis import init_model, sample_unconditional_model config_file = 'configs/styleganv2/stylegan2_c2_8xb4-800kiters_lsun-church-256x256.py' # you can download this checkpoint in advance and use a local file path. -checkpoint_file = 'https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth' +checkpoint_file = 'https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth' device = 'cuda:0' # init a generative model model = init_model(config_file, checkpoint_file, device=device) @@ -22,7 +22,7 @@ Or you can just run the following command. ```bash python demo/unconditional_demo.py \ configs/styleganv2/stylegan2_c2_lsun-church_256_b4x8_800k.py \ -https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth +https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth ``` diff --git a/docs/en/howto/models.md b/docs/en/howto/models.md index a09fdf0288..d7e93eb4e5 100644 --- a/docs/en/howto/models.md +++ b/docs/en/howto/models.md @@ -425,7 +425,7 @@ model = dict( )) ``` -We also need to specify the training dataloader and testing dataloader according to [create your own dataloader](../dataset_zoo/overview.md). +We also need to specify the training dataloader and testing dataloader according to create your own dataloader. Finally we can start training our own model by： ```python diff --git a/docs/en/user_guides/dataset_prepare.md b/docs/en/user_guides/dataset_prepare.md index fe208aee9c..b26ae391cf 100644 --- a/docs/en/user_guides/dataset_prepare.md +++ b/docs/en/user_guides/dataset_prepare.md @@ -34,6 +34,6 @@ python tools/dataset_converters/super-resolution/div2k/preprocess_div2k_dataset. We support detailed tutorials and split them according to different tasks. -Please check our [dataset zoo](../dataset_zoo/overview.md) for data preparation of different tasks. +Please check our dataset zoo for data preparation of different tasks. If you're interested in more details of datasets in MMEditing, please check the [advanced guides](../howto/dataset.md). diff --git a/docs/en/user_guides/inference.md b/docs/en/user_guides/inference.md index dcb2e6efe3..cc71aaf608 100644 --- a/docs/en/user_guides/inference.md +++ b/docs/en/user_guides/inference.md @@ -30,7 +30,7 @@ from mmedit.apis import init_model, sample_unconditional_model # Specify the path to model config and checkpoint file config_file = 'configs/styleganv2/stylegan2_c2_8xb4_ffhq-1024x1024.py' # you can download this checkpoint in advance and use a local file path. -checkpoint_file = 'https://download.openmmlab.com/mmgen/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth' +checkpoint_file = 'https://download.openmmlab.com/mmediting/stylegan2/stylegan2_c2_ffhq_1024_b4x8_20210407_150045-618c9024.pth' device = 'cuda:0' # init a generative model @@ -61,7 +61,7 @@ from mmedit.apis import init_model, sample_conditional_model # Specify the path to model config and checkpoint file config_file = 'configs/sagan/sagan_woReLUinplace-Glr1e-4_Dlr4e-4_noaug-ndisc1-8xb32-bigGAN-sch_imagenet1k-128x128.py' # you can download this checkpoint in advance and use a local file path. -checkpoint_file = 'https://download.openmmlab.com/mmgen/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth' +checkpoint_file = 'https://download.openmmlab.com/mmediting/sagan/sagan_128_woReLUinplace_noaug_bigGAN_imagenet1k_b32x8_Glr1e-4_Dlr-4e-4_ndisc1_20210818_210232-3f5686af.pth' device = 'cuda:0' # init a generative model @@ -105,7 +105,7 @@ from mmedit.apis import init_model, sample_ddpm_model # Specify the path to model config and checkpoint file config_file = 'configs/improved_ddpm/ddpm_cosine-hybird-timestep-4k_16xb8-1500kiters_imagenet1k-64x64.py' # you can download this checkpoint in advance and use a local file path. -checkpoint_file = 'https://download.openmmlab.com/mmgen/improved_ddpm/ddpm_cosine_hybird_timestep-4k_imagenet1k_64x64_b8x16_1500k_20220103_223919-b8f1a310.pth' +checkpoint_file = 'https://download.openmmlab.com/mmediting/improved_ddpm/ddpm_cosine_hybird_timestep-4k_imagenet1k_64x64_b8x16_1500k_20220103_223919-b8f1a310.pth' device = 'cuda:0' # init a generative model model = init_model(config_file, checkpoint_file, device=device) @@ -332,7 +332,7 @@ from mmedit.apis import init_model, sample_img2img_model # Specify the path to model config and checkpoint file config_file = 'configs/pix2pix/pix2pix_vanilla-unet-bn_wo-jitter-flip-4xb1-190kiters_edges2shoes.py' # you can download this checkpoint in advance and use a local file path. -checkpoint_file = 'https://download.openmmlab.com/mmgen/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth' +checkpoint_file = 'https://download.openmmlab.com/mmediting/pix2pix/refactor/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_186840_edges2shoes_convert-bgr_20210902_170902-0c828552.pth' # Specify the path to image you want to translate image_path = 'tests/data/paired/test/33_AB.jpg' device = 'cuda:0' diff --git a/docs/zh_cn/community/contributing.md b/docs/zh_cn/community/contributing.md index b446d27cc2..028e00a4a9 100644 --- a/docs/zh_cn/community/contributing.md +++ b/docs/zh_cn/community/contributing.md @@ -228,14 +228,14 @@ make html - [mdformat](https://github.com/executablebooks/mdformat): 检查 markdown 文件的工具 - [docformatter](https://github.com/myint/docformatter): 格式化 docstring 的工具 -yapf 和 isort 的配置可以在 [setup.cfg](../../setup.cfg) 找到 +yapf 和 isort 的配置可以在 [setup.cfg](../../../setup.cfg) 找到 通过配置 [pre-commit hook](https://pre-commit.com/) ，我们可以在提交代码时自动检查和格式化 `flake8`、`yapf`、`isort`、`trailing whitespaces`、`markdown files`，修复 `end-of-files`、`double-quoted-strings`、`python-encoding-pragma`、`mixed-line-ending`，调整 `requirments.txt` 的包顺序。 -pre-commit 钩子的配置可以在 [.pre-commit-config](../../.pre-commit-config.yaml) 找到。 +pre-commit 钩子的配置可以在 [.pre-commit-config](../../../.pre-commit-config.yaml) 找到。 pre-commit 具体的安装使用方式见[拉取请求](#2-配置-pre-commit)。 -更具体的规范请参考 [OpenMMLab 代码规范](#代码风格)。 +更具体的规范请参考 [OpenMMLab 代码规范](contributing.md#代码风格)。 #### C++ and CUDA @@ -336,7 +336,7 @@ from mmedit.cnn.bricks import Conv2d, build_norm_layer, DropPath, MaxPool2d, \ from ...utils import is_str # 最多向上回溯一层，过多的回溯容易导致结构混乱 ``` -OpenMMLab 项目使用 pre-commit 工具自动格式化代码，详情见[贡献代码](./contributing.md#代码风格)。 +OpenMMLab 项目使用 pre-commit 工具自动格式化代码，详情见[贡献代码](contributing.md#代码风格)。 ### 命名规范 diff --git a/mmedit/models/editors/glean/glean_styleganv2.py b/mmedit/models/editors/glean/glean_styleganv2.py index 4a806ceff8..54aa2cae0b 100644 --- a/mmedit/models/editors/glean/glean_styleganv2.py +++ b/mmedit/models/editors/glean/glean_styleganv2.py @@ -29,11 +29,11 @@ class GLEANStyleGANv2(BaseModule): ``pretrained`` argument. We have already offered official weights as follows: - - styelgan2-ffhq-config-f: http://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa - - stylegan2-horse-config-f: http://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa - - stylegan2-car-config-f: http://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa - - styelgan2-cat-config-f: http://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa - - stylegan2-church-config-f: http://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa + - styelgan2-ffhq-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa + - stylegan2-horse-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa + - stylegan2-car-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa + - styelgan2-cat-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa + - stylegan2-church-config-f: http://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa If you want to load the ema model, you can just use following codes: diff --git a/mmedit/models/editors/stylegan2/stylegan2_discriminator.py b/mmedit/models/editors/stylegan2/stylegan2_discriminator.py index 5c993b6540..f752fa4055 100644 --- a/mmedit/models/editors/stylegan2/stylegan2_discriminator.py +++ b/mmedit/models/editors/stylegan2/stylegan2_discriminator.py @@ -31,11 +31,11 @@ class StyleGAN2Discriminator(BaseModule): ``pretrained`` argument. We have already offered official weights as follows: - - stylegan2-ffhq-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa - - stylegan2-horse-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa - - stylegan2-car-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa - - stylegan2-cat-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa - - stylegan2-church-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa + - stylegan2-ffhq-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa + - stylegan2-horse-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa + - stylegan2-car-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa + - stylegan2-cat-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa + - stylegan2-church-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa If you want to load the ema model, you can just use following codes: diff --git a/mmedit/models/editors/stylegan2/stylegan2_generator.py b/mmedit/models/editors/stylegan2/stylegan2_generator.py index aec58d91e8..fd83c08c11 100644 --- a/mmedit/models/editors/stylegan2/stylegan2_generator.py +++ b/mmedit/models/editors/stylegan2/stylegan2_generator.py @@ -29,11 +29,11 @@ class StyleGAN2Generator(nn.Module): ``pretrained`` argument. We have already offered official weights as follows: - - stylegan2-ffhq-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa - - stylegan2-horse-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa - - stylegan2-car-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa - - stylegan2-cat-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa - - stylegan2-church-config-f: https://download.openmmlab.com/mmgen/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa + - stylegan2-ffhq-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-ffhq-config-f-official_20210327_171224-bce9310c.pth # noqa + - stylegan2-horse-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-horse-config-f-official_20210327_173203-ef3e69ca.pth # noqa + - stylegan2-car-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-car-config-f-official_20210327_172340-8cfe053c.pth # noqa + - stylegan2-cat-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-cat-config-f-official_20210327_172444-15bc485b.pth # noqa + - stylegan2-church-config-f: https://download.openmmlab.com/mmediting/stylegan2/official_weights/stylegan2-church-config-f-official_20210327_172657-1d42b7d1.pth # noqa If you want to load the ema model, you can just use following codes: