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Update weights and re-evaluate models (#274)
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* Update weights and re-evaluate models

* Update links to weights

* Add back SSIM to configuration
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@ckkelvinchan
ckkelvinchan authored Apr 25, 2021
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8 changes: 4 additions & 4 deletions configs/restorers/basicvsr/README.md
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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) .
The pretrained weights of SPyNet can be found [here](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth).


| Method | REDS4 (BIx4)<br>PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4)<br>PSNR/SSIM (Y) | Vid4 (BIx4)<br>PSNR/SSIM (Y) | UDM10 (BDx4)<br>PSNR/SSIM (Y) | Vimeo-90K-T (BDx4)<br>PSNR/SSIM (Y) | Vid4 (BDx4)<br>PSNR/SSIM (Y) | Download |
|:------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------:|:-----------------------------------:|:----------------------------:|:-----------------------------:|:-----------------------------------:|:----------------------------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [basicvsr_reds4](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/basicvsr_reds4.py) | **31.4170/0.8909** | 36.2870/0.9388 | 27.2223/0.8298 | 33.4510/0.9297 | 34.5053/0.9280 | 24.4390/0.7441 | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20120409-089ec9ef.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_reds4_20210409_092646.log.json) |
| [basicvsr_vimeo90k_bi](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/basicvsr_vimeo90k_bi.py) | 30.3128/0.8660 | **37.2299/0.9447** | **27.2296/0.8227** | 34.5488/0.9423 | 34.8713/0.9313 | 25.0377/0.7622 | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409-7b3e0c43.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bi_20210409_132702.log.json) |
| [basicvsr_vimeo90k_bd](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/basicvsr_vimeo90k_bd.py) | 29.0376/0.8481 | 34.7094/0.9332 | 26.2356/0.8000 | **39.8802/0.9683** | **37.5730/0.9495** | **27.9278/0.8537** | [model](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409-6b10d46a.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/basicvsr/basicvsr_vimeo90k_bd_20210409_132740.log.json) |
| [basicvsr_reds4](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/basicvsr_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 | [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](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/basicvsr_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 | [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](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/basicvsr/basicvsr_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** | [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) |
2 changes: 1 addition & 1 deletion configs/restorers/basicvsr/basicvsr_reds4.py
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pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))
# model training and testing settings
train_cfg = dict(fix_iter=5000)
test_cfg = dict(metrics=['PSNR'], crop_border=0)
test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)

# dataset settings
train_dataset_type = 'SRREDSMultipleGTDataset'
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2 changes: 1 addition & 1 deletion configs/restorers/basicvsr/basicvsr_vimeo90k_bd.py
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pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))
# model training and testing settings
train_cfg = dict(fix_iter=5000)
test_cfg = dict(metrics=['PSNR'], crop_border=0, convert_to='y')
test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0, convert_to='y')

# dataset settings
train_dataset_type = 'SRVimeo90KMultipleGTDataset'
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2 changes: 1 addition & 1 deletion configs/restorers/basicvsr/basicvsr_vimeo90k_bi.py
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pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))
# model training and testing settings
train_cfg = dict(fix_iter=5000)
test_cfg = dict(metrics=['PSNR'], crop_border=0, convert_to='y')
test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0, convert_to='y')

# dataset settings
train_dataset_type = 'SRVimeo90KMultipleGTDataset'
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8 changes: 4 additions & 4 deletions configs/restorers/iconvsr/README.md
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Evaluated on RGB channels for REDS4 and Y channel for others. The metrics are `PSNR`/`SSIM`.

The pretrained weights of the IconVSR components can be found here: [SPyNet](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth), [EDVR-M for REDS](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_reds_20210413-3867262f.pth), and [EDVR-M for Vimeo-90K](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_vimeo90k_20210413-e40e99a8.pth) .
The pretrained weights of the IconVSR components can be found here: [SPyNet](https://download.openmmlab.com/mmediting/restorers/basicvsr/spynet_20210409-c6c1bd09.pth), [EDVR-M for REDS](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_reds_20210413-3867262f.pth), and [EDVR-M for Vimeo-90K](https://download.openmmlab.com/mmediting/restorers/iconvsr/edvrm_vimeo90k_20210413-e40e99a8.pth).


| Method | REDS4 (BIx4)<br>PSNR/SSIM (RGB) | Vimeo-90K-T (BIx4)<br>PSNR/SSIM (Y) | Vid4 (BIx4)<br>PSNR/SSIM (Y) | UDM10 (BDx4)<br>PSNR/SSIM (Y) | Vimeo-90K-T (BDx4)<br>PSNR/SSIM (Y) | Vid4 (BDx4)<br>PSNR/SSIM (Y) | Download |
|:---------------------------------------------------------------------------------------------------------------------------:|:-------------------------------:|:-----------------------------------:|:----------------------------:|:-----------------------------:|:-----------------------------------:|:----------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| [iconvsr_reds4](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/iconvsr/iconvsr_reds.py) | **31.6926/0.8951** | 36.4963/0.9410 | **27.4350/0.8334** | 35.3336/0.9463 | 34.4626/0.9280 | 25.2045/0.7721 | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413-74343d4f.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_reds4_20210413_222735.log.json) |
| [iconvsr_vimeo90k_bi](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/iconvsr/iconvsr_vimeo90k_bi.py) | 30.3452/0.8659 | **37.4008/0.9464** | 27.3738/0.8274 | 34.2639/0.9388 | 34.6193/0.9293 | 24.6648/0.7480 | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413-ba2ffd72.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bi_20210413_222757.log.json) |
| [iconvsr_vimeo90k_bd](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/iconvsr/iconvsr_vimeo90k_bd.py) | 29.0150/0.8465 | 34.7454/0.9336 | 26.2763/0.8006 | **39.9403/0.8006** | **37.7794/0.9513** | **28.1916/0.8592** | [model](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414-292a63ac.pth) \| [log](https://download.openmmlab.com/mmediting/restorers/iconvsr/iconvsr_vimeo90k_bd_20210414_084128.log.json) |
| [iconvsr_reds4](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/iconvsr/iconvsr_reds.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 | [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](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/iconvsr/iconvsr_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 | [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](https://github.com/open-mmlab/mmediting/blob/master/configs/restorers/iconvsr/iconvsr_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** | [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) |
2 changes: 1 addition & 1 deletion configs/restorers/iconvsr/iconvsr_reds4.py
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pixel_loss=dict(type='CharbonnierLoss', loss_weight=1.0, reduction='mean'))
# model training and testing settings
train_cfg = dict(fix_iter=5000)
test_cfg = dict(metrics=['PSNR'], crop_border=0)
test_cfg = dict(metrics=['PSNR', 'SSIM'], crop_border=0)

# dataset settings
train_dataset_type = 'SRREDSMultipleGTDataset'
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2 changes: 1 addition & 1 deletion configs/restorers/iconvsr/iconvsr_vimeo90k_bd.py
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type=test_dataset_type,
lq_folder='data/vimeo90k/BDx4',
gt_folder='data/vimeo90k/GT',
ann_file='data/Vimeo90K/meta_info_Vimeo90K_test_GT.txt',
ann_file='data/vimeo90k/meta_info_Vimeo90K_test_GT.txt',
pipeline=test_pipeline,
scale=4,
num_input_frames=7,
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2 changes: 1 addition & 1 deletion configs/restorers/iconvsr/iconvsr_vimeo90k_bi.py
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type=test_dataset_type,
lq_folder='data/vimeo90k/BIx4',
gt_folder='data/vimeo90k/GT',
ann_file='data/Vimeo90K/meta_info_Vimeo90K_test_GT.txt',
ann_file='data/vimeo90k/meta_info_Vimeo90K_test_GT.txt',
pipeline=test_pipeline,
scale=4,
num_input_frames=7,
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