- Try the application
- Sketch to Color Image generation is an image-to-image translation model using Conditional Generative Adversarial Networks
- This project is developed with DVC pipeline
- Dataset used - Anime Sketch Colorization Pair\
- Model is only trained for 35 epochs on Google Colab
- You can train it according to your need by changing parameters in params.yaml file
- U-Net architecture is followed for building Generator and Discriminator Models.
- Create a new virtual environment.
- Python 3.6 or greater.
- DVC
- Run Command-
pip install -r requirements.txt
- Change parameters and directories according to your system (recommendation - do not change) in params.yaml.
- Setup the Directory named Results in the root directory.
- Make sure you have DVC initialized in the root directory
You can do it with the command:
dvc init
- Add data\train & data\val for data tracking in dvc by using command: For Windows-
dvc add data\train
dvc add data\val
For Linux-
dvc add data/train
dvc add data/val
- Finally, run:
dvc repro
- Trained models will be saved in saved_models directory.
- Results will be saved in Results\present_datatime directory with name present_time_result.png.
Warning: Do not try to change dvc.lock file and .dvc directory
