tahamajs/cifar10-inpainting-complex-unet

U-Net for Image Inpainting on CIFAR-10

This repository contains a PyTorch implementation of a deep U-Net with Residual Blocks, trained to perform image inpainting on the CIFAR-10 dataset. The model takes a 32x32 image with a masked (blacked-out) region and reconstructs the missing part.

Original	Masked Input	Reconstructed Output

Note: The images above are illustrative examples. You can generate your own by running the code below.

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Model Architecture

The model is a ComplexUNet, a variant of the standard U-Net architecture, designed to be deeper and wider for improved performance.

• Framework: PyTorch
• Architecture: U-Net with 4 downsampling and 4 upsampling stages.
• Backbone: Each stage uses Residual Blocks instead of simple convolutional layers.
• Model Width: The number of base channels is increased to 96 for higher capacity.
• Total Parameters: 73,148,259

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How to Use

The following code snippet provides a complete example of how to load the model, process an image from the CIFAR-10 test set, and visualize the inpainting result.

First, ensure you have the necessary libraries installed:

pip install torch torchvision numpy matplotlib Pillow