https://github.com/hayashi-yudai/srgan
Super resolution GAN
https://github.com/hayashi-yudai/srgan
computer-vision gan machine-learning python3 super-resolution tensorflow
Last synced: 2 months ago
JSON representation
Super resolution GAN
- Host: GitHub
- URL: https://github.com/hayashi-yudai/srgan
- Owner: Hayashi-Yudai
- Created: 2021-08-23T13:56:05.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2022-10-16T11:24:04.000Z (over 3 years ago)
- Last Synced: 2026-02-08T06:12:54.516Z (5 months ago)
- Topics: computer-vision, gan, machine-learning, python3, super-resolution, tensorflow
- Language: Python
- Homepage:
- Size: 1.94 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# SRGAN
## Verification environment
- Python 3.10.4
- Tensorflow 2.10.0
## Network structure
cited from https://arxiv.org/pdf/1609.04802.pdf
## How to train
### Prepare your dataset
Make the following directory tree for your dataset on the project root and place original images in `train/high_resolution` and `validate/high_resolution/` directories.
```
.datasets
└── (your dataset name)
├── test
│ ├── high_resolution
│ └── low_resolution
├── train
│ ├── high_resolution
│ └── low_resolution
└── validate
├── high_resolution
└── low_resolution
```
Next, make low resolution images which have quarter size of original ones and place them in `low_resolution` directories.
This program request TFRecords as dataset. I prepare a function for you. Fix `dataset_name` and `extension` in the src/datasets.py and execute it from project root.
```bash
poetry run python src/dataset.py
```
Make sure there exists `train.tfrecords` and `valid.tfrecords` in the `datasets/(your dataset name)` directory.
### Configure parameters
The parameters like hyper-parameters are set in the config.yaml
### Training
#### Pre-training SRResNet
config.yaml
```
TYPE: SRResNet
EPOCHS: 10000
BATCH_SIZE: 16
IMG_HEIGHT: 32
IMG_WIDTH: 32
LEARNING_RATE: 0.0001
TRAIN_DATA_PATH: ./datasets/train.tfrecords
VALIDATE_DATA_PATH: ./datasets/valid.tfrecords
CHECKPOINT_PATH: ./checkpoint/generator_train
START_EPOCH: 0
GEN_WEIGHT:
DISC_WEIGHT:
G_LOSS: 100000000
```
Start training with the following command
```bash
$ poetry run python src/train.py
```
#### Training SRGAN
config.yaml
```
TYPE: SRGAN
EPOCHS: 10000
BATCH_SIZE: 16
IMG_HEIGHT: 32
IMG_WIDTH: 32
LEARNING_RATE: 0.0001
TRAIN_DATA_PATH: ./datasets/train.tfrecords
VALIDATE_DATA_PATH: ./datasets/valid.tfrecords
CHECKPOINT_PATH: ./checkpoint/gan_train
START_EPOCH: 0
GEN_WEIGHT: ./generator_train/generator_best
DISC_WEIGHT:
G_LOSS: 100000000
```
Start training with the following command
```bash
$ poetry run python train
```
## Example
You can download pre-trained weight from [here](https://drive.google.com/drive/folders/1LE1AK0HVHN-_x9S3-aCUeA0mFT-D68VW?usp=sharing). These weights are trained with 32x32 images.
