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PVTv2

README.md

PVTv2: Improved Baselines with Pyramid Vision Transformer, arxiv

PaddlePaddle training/validation code and pretrained models for PVTv2.

The official pytorch implementation is here.

This implementation is developed by PaddleViT.

drawing

PVTv2 Model Overview

Update

  • Update (2022-03-17): Code is refactored and bugs are fixed.
  • Update (2021-09-27): Model FLOPs and # params are uploaded.
  • Update (2021-08-11): Code is released and ported weights are uploaded.

Models Zoo

Model Acc@1 Acc@5 #Params FLOPs Image Size Crop_pct Interpolation Link
pvtv2_b0 70.47 90.16 3.7M 0.6G 224 0.875 bicubic google/baidu
pvtv2_b1 78.70 94.49 14.0M 2.1G 224 0.875 bicubic google/baidu
pvtv2_b2 82.02 95.99 25.4M 4.0G 224 0.875 bicubic google/baidu
pvtv2_b2_linear 82.06 96.04 22.6M 3.9G 224 0.875 bicubic google/baidu
pvtv2_b3 83.14 96.47 45.2M 6.8G 224 0.875 bicubic google/baidu
pvtv2_b4 83.61 96.69 62.6M 10.0G 224 0.875 bicubic google/baidu
pvtv2_b5 83.77 96.61 82.0M 11.5G 224 0.875 bicubic google/baidu

*The results are evaluated on ImageNet2012 validation set.

Data Preparation

ImageNet2012 dataset is used in the following file structure:

│imagenet/
├──train_list.txt
├──val_list.txt
├──train/
│  ├── n01440764
│  │   ├── n01440764_10026.JPEG
│  │   ├── n01440764_10027.JPEG
│  │   ├── ......
│  ├── ......
├──val/
│  ├── n01440764
│  │   ├── ILSVRC2012_val_00000293.JPEG
│  │   ├── ILSVRC2012_val_00002138.JPEG
│  │   ├── ......
│  ├── ......
  • train_list.txt: list of relative paths and labels of training images. You can download it from: google/baidu
  • val_list.txt: list of relative paths and labels of validation images. You can download it from: google/baidu

Usage

To use the model with pretrained weights, download the .pdparam weight file and change related file paths in the following python scripts. The model config files are located in ./configs/.

For example, assume weight file is downloaded in ./pvtv2_b0.pdparams, to use the pvtv2_b0 model in python:

from config import get_config
from pvtv2 import build_pvtv2 as build_model
# config files in ./configs/
config = get_config('./configs/pvtv2_b0.yaml')
# build model
model = build_model(config)
# load pretrained weights
model_state_dict = paddle.load('./pvtv2_b0.pdparams')
model.set_state_dict(model_state_dict)

Evaluation

To evaluate PVTv2 model performance on ImageNet2012, run the following script using command line:

sh run_eval_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/pvtv2_b0.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-eval \
-pretrained='./pvtv2_b0.pdparams' \
-amp

Note: if you have only 1 GPU, change device number to CUDA_VISIBLE_DEVICES=0 would run the evaluation on single GPU.

Training

To train the PVTv2 model on ImageNet2012, run the following script using command line:

sh run_train_multi.sh

or

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
python main_multi_gpu.py \
-cfg='./configs/pvtv2_b0.yaml' \
-dataset='imagenet2012' \
-batch_size=256 \
-data_path='/dataset/imagenet' \
-amp

Note: it is highly recommanded to run the training using multiple GPUs / multi-node GPUs.

Reference

@article{wang2021pvtv2,
  title={Pvtv2: Improved baselines with pyramid vision transformer},
  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
  journal={arXiv preprint arXiv:2106.13797},
  year={2021}
}