Implementation of CVPR2017 workshop Paper: "Enhanced Deep Residual Networks for Single Image Super-Resolution"(https://arxiv.org/pdf/1707.02921.pdf) in PyTorch
usage: main_edsr.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
[--step STEP] [--cuda] [--resume RESUME]
[--start-epoch START_EPOCH] [--threads THREADS]
[--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
optional arguments:
-h, --help show this help message and exit
--batchSize BATCHSIZE
training batch size
--nEpochs NEPOCHS number of epochs to train for
--lr LR Learning Rate. Default=1e-4
--step STEP Sets the learning rate to the initial LR decayed by
momentum every n epochs, Default: n=10
--cuda use cuda?
--resume RESUME path to latest checkpoint (default: none)
--start-epoch START_EPOCH
manual epoch number (useful on restarts)
--threads THREADS number of threads for data loader to use
--momentum MOMENTUM momentum
--weight-decay WEIGHT_DECAY, --wd WEIGHT_DECAY
weight decay, Default: 0
usage: test.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE]
PyTorch EDSR Test
optional arguments:
-h, --help show this help message and exit
--cuda use cuda?
--model MODEL model path
--image IMAGE image name
--scale SCALE scale factor, Default: 4
- We convert Set5 test set images to mat format using Matlab, for best PSNR performance, please use Matlab
- An example of usage is shown as follows:
python test.py --model model/model_edsr.pth --image butterfly_GT --scale 4 --cuda
usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
[--scale SCALE]
PyTorch ByNet Eval
optional arguments:
-h, --help Show this help message and exit
--cuda use cuda?
--model MODEL Model path. Default=model/model_epoch_40.pth
--dataset DATASET Dataset name, Default: Set5
- An trained model on 291 images can be downloaded at google_drive, which could achieve 31.94dB PNSR on Set5 dataset.
- An example of training usage is shown as follows:
python eval.py --cuda --model model/model_edsr.pth --dataset Set5
- Please refer Code for Data Generation for creating training files.
- Data augmentations including flipping, rotation, downsizing are adopted.
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