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TDAN (CVPR'2020)

TDAN (CVPR'2020) 
@InProceedings{tian2020tdan,
  title={TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution},
  author={Tian, Yapeng and Zhang, Yulun and Fu, Yun and Xu, Chenliang},
  booktitle = {Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  year = {2020}
}

Evaluated on Y-channel. 8 pixels in each border are cropped before evaluation. The metrics are PSNR / SSIM .

Method Vid4 (BIx4) SPMCS-30 (BIx4) Vid4 (BDx4) SPMCS-30 (BDx4) Download
tdan_vimeo90k_bix4 26.49/0.792 30.42/0.856 25.93/0.772 29.69/0.842 model | log
tdan_vimeo90k_bdx4 25.80/0.784 29.56/0.851 26.87/0.815 30.77/0.868 model | log

Train

Train Instructions

You can use the following command to train a model.

./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [optional arguments]

TDAN is trained with two stages.

Stage 1: Train with a larger learning rate (1e-4)

./tools/dist_train.sh configs/restorers/tdan/tdan_vimeo90k_bix4_lr1e-4_400k.py 8

Stage 2: Fine-tune with a smaller learning rate (5e-5)

./tools/dist_train.sh configs/restorers/tdan/tdan_vimeo90k_bix4_ft_lr5e-5_400k.py 8

For more details, you can refer to Train a model part in getting_started.

Test

Test Instructions

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]

Example: Test TDAN on SPMCS-30 using Bicubic downsampling.

python tools/test.py configs/restorers/tdan/tdan_vimeo90k_bix4_ft_lr5e-5_400k.py  checkpoints/SOME_CHECKPOINT.pth --save_path outputs/

For more details, you can refer to Inference with pretrained models part in getting_started.