Welcome to the Image Inpaint repository! This repository is dedicated to image inpainting techniques and experiments using various machine learning models, including U-Net, xLSTM, and GAN-based methods.
- Implementation of multiple image inpainting architectures.
- Comprehensive experiments and evaluations.
- Jupyter notebooks for easy experimentation and customization.
/notebooks/: Contains experimental Jupyter notebooks for different inpainting models./data/: Example datasets for training and testing./models/: Saved model architectures and weights./utils/: Helper preprocessing, visualization, and evaluation scripts.requirements.txt: Python dependencies.
- U-Net: A baseline architecture for inpainting.
- xLSTM: Enhanced temporal models for sequential inpainting.
- GANs: Generative adversarial networks for high-quality results.
- Clone the repository:
git clone https://github.com/fjadidi2001/Image_Inpaint.git
Feel free to submit issues or pull requests to improve the repository. Contributions are welcome!
This script/notebook implements the <model_name> approach for image inpainting. It focuses on restoring missing regions in images using advanced machine-learning techniques.
- Implements
<specific_feature>. - Provides results visualizations.
- Adjustable hyperparameters for experimentation.
- Ensure the dataset is prepared.
- Run the file:
jupyter notebook <notebook_name>.ipynb