datasets:
- phiyodr/coco2017
language:
- en
library_name: fastai
pipeline_tag: image-to-image
Model Card: GAN Colorization Model Model Description This GAN-based model performs image colorization, transforming grayscale images into color images. It leverages a generator network to predict the color channels and a discriminator network to improve the colorization quality through adversarial training.
Model Details Model Name: GAN Colorization Model Model Architecture: The model uses a ResNet-34 backbone as the encoder in the generator network and a PatchGAN discriminator network. Framework: PyTorch Repository: Hammad712/GAN-Colorization-Model Model Training Dataset Dataset Used: COCO 2017 Training Size: 8000 images Validation Size: 2000 images Image Size: 256x256 pixels Training Configuration Batch Size: 16 Number of Epochs: 5 Optimizer for Generator: Adam (learning rate: 0.0004, betas: 0.5, 0.999) Optimizer for Discriminator: Adam (learning rate: 0.0004, betas: 0.5, 0.999) Loss Functions: GAN Loss: Binary Cross-Entropy Loss with Logits L1 Loss: L1 Loss for pixel-wise comparison between generated and real color channels
Usage To use the model for image colorization, ensure that the dependencies are installed and run the inference code provided. You will need to replace the image path with your own image for colorization.
Model Performance Qualitative Results The model generates visually plausible colorizations for grayscale images. Here are some examples of colorized outputs:
Limitations The model may not always produce accurate colors for objects with complex or unusual color distributions. Performance may degrade for images that significantly differ from the training dataset. Citation If you use this model in your research, please cite the original repository:
bibtex Copy code @misc{Hammad7122023GANColorization, title={GAN-Colorization-Model}, author={Hammad712}, year={2023}, publisher={Hugging Face}, howpublished={\url{https://huggingface.co/Hammad712/GAN-Colorization-Model}}, } Contact For any issues or inquiries, please reach out to the model author through the Hugging Face repository.