SRResnet & SRGAN implementations.
I used VOC2012 with 17k pair of images (LR, HR) to train models. (LR, HR) with size (22x22) upto (88x88) with scale factor = 4.
Then test phase with 3 benchmark datasets: BDS100, Set14, Set5.
Here is the result after 50 epochs with default setting.
Note: PSNR/SSIM
| BDS100 | SET14 | SET5 | |
|---|---|---|---|
| SRResnet | 26.68/0.82 | 30.87/0.94 | 27.46/0.86 |
| SRGAN | 26.07/0.81 | 29.36/0.92 | 26.49/0.85 |
- Git clone this repo
- Install requirements
- Create checkpoint folder inside
- Prepocessing data
- Train and Inference
You can change the setting directly in train_SRResnet.py or train_SRGAN.py.
Run with default setting.
python train_SRResnet.py
python train_SRGAN.py
To inference
python inference.py
I compressed dataset in .pkl file for training on google colab. Check notebook for more details. Pkl saving format: List of np array.
Download pkl file with demo [1000/300]: