PyTorch VDSR
Implementation of CVPR2016 Paper: "Accurate Image Super-Resolution Using Very Deep Convolutional Networks"(http://cv.snu.ac.kr/research/VDSR/) in PyTorch
Usage
Training
usage: main_vdsr.py [-h] [--batchSize BATCHSIZE] [--nEpochs NEPOCHS] [--lr LR]
[--step STEP] [--cuda] [--resume RESUME]
[--start-epoch START_EPOCH] [--clip CLIP] [--threads THREADS]
[--momentum MOMENTUM] [--weight-decay WEIGHT_DECAY]
[--pretrained PRETRAINED] [--gpus GPUS]
optional arguments:
-h, --help Show this help message and exit
--batchSize Training batch size
--nEpochs Number of epochs to train for
--lr Learning rate. Default=0.01
--step Learning rate decay, Default: n=10 epochs
--cuda Use cuda
--resume Path to checkpoint
--clip Clipping Gradients. Default=0.4
--threads Number of threads for data loader to use Default=1
--momentum Momentum, Default: 0.9
--weight-decay Weight decay, Default: 1e-4
--pretrained PRETRAINED
path to pretrained model (default: none)
--gpus GPUS gpu ids (default: 0)
An example of training usage is shown as follows:
python main_vdsr.py --cuda --gpus 0
Evaluation
usage: eval.py [-h] [--cuda] [--model MODEL] [--dataset DATASET]
[--scale SCALE] [--gpus GPUS]
PyTorch VDSR Eval
optional arguments:
-h, --help show this help message and exit
--cuda use cuda?
--model MODEL model path
--dataset DATASET dataset name, Default: Set5
--gpus GPUS gpu ids (default: 0)
An example of training usage is shown as follows:
python eval.py --cuda --dataset Set5
Demo
usage: demo.py [-h] [--cuda] [--model MODEL] [--image IMAGE] [--scale SCALE] [--gpus GPUS]
optional arguments:
-h, --help Show this help message and exit
--cuda Use cuda
--model Model path. Default=model/model_epoch_50.pth
--image Image name. Default=butterfly_GT
--scale Scale factor, Default: 4
--gpus GPUS gpu ids (default: 0)
An example of usage is shown as follows:
python eval.py --model model/model_epoch_50.pth --dataset Set5 --cuda
Prepare Training dataset
- We provide a simple hdf5 format training sample in data folder with 'data' and 'label' keys, the training data is generated with Matlab Bicubic Interplotation, please refer Code for Data Generation for creating training files.
Performance
- We provide a pretrained VDSR model trained on 291 images with data augmentation
- No bias is used in this implementation, and the gradient clipping's implementation is different from paper
- Performance in PSNR on Set5
| Scale | VDSR Paper | VDSR PyTorch |
|---|---|---|
| 2x | 37.53 | 37.65 |
| 3x | 33.66 | 33.77 |
| 4x | 31.35 | 31.45 |
Result
From left to right are ground truth, bicubic and vdsr
