Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
VDSR (CVPR2016) pytorch implementation
| Date | Stars |
|---|---|
| 2026-07-24 | 453 |
| 2026-07-25 | 453 |
| 2026-07-28 | 453 |
| 2026-07-30 | 453 |
| 2026-08-06 | 453 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# 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](https://github.com/twtygqyy/pytorch-vdsr/tree/master/data) for creating training files.
### Performance
- We provide a pretrained VDSR model trained on [291](https://drive.google.com/open?id=1Rt3asDLuMgLuJvPA1YrhyjWhb97Ly742) 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
<p>
<img src='Set5/butterfly_GT.bmp' height='200' width='200'/>
<img src='result/input.bmp' height='200' width='200'/>
<img src='result/output.bmp' height='200' width='200'/>
</p>
Excerpt of 3,512 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7ac5ef61433e3fcd, topic:super-resolution, readme:super resolution
matched fp:7ac5ef61433e3fcd, topic:pytorch