| # Handwritten Digit Generator | |
| A web application that generates handwritten digit images using a trained Conditional GAN model. | |
| ## Time Constraints & Future Improvements | |
| Due to time constraints during this contest, the model was trained for fewer epochs than ideal. Given more time, the following improvements and additional training steps would have been performed: | |
| - **Extended Training**: Train the GAN for more epochs (50-100 instead of 10) to improve image quality and diversity | |
| - **Hyperparameter Optimization**: Experiment with different learning rates, batch sizes, and optimizer settings | |
| - **Advanced GAN Techniques**: | |
| - Implement label smoothing for more stable training | |
| - Add noise injection to discriminator inputs | |
| - Use progressive growing or spectral normalization | |
| - **Architecture Improvements**: | |
| - Increase the size and complexity of generator and discriminator networks | |
| - Explore ResNet or U-Net based architectures | |
| - Add self-attention mechanisms | |
| - **Training Stability**: | |
| - Implement Wasserstein loss or LSGAN loss functions | |
| - Use feature matching and minibatch discrimination | |
| - Apply gradient penalty techniques | |
| - **Evaluation & Fine-tuning**: | |
| - Perform more extensive evaluation using FID and IS scores | |
| - Fine-tune to reduce artifacts and improve digit clarity | |
| - Add more sophisticated conditioning mechanisms | |
| - **Alternative Architectures**: | |
| - Explore Conditional GANs with attention mechanisms | |
| - Implement VAE-GAN hybrid models | |
| - Try diffusion-based generation approaches | |
| ## Current Results | |
| Despite the limited training time, the model successfully generates recognizable digits for all classes (0-9) with reasonable diversity, meeting the contest requirements. | |
| ## Technical Details | |
| - **Framework**: PyTorch | |
| - **Dataset**: MNIST (28x28 grayscale) | |
| - **Architecture**: Conditional GAN with label embedding | |
| - **Training**: 10 epochs on Google Colab T4 GPU | |
| - **Interface**: Gradio web application | |
| - **Deployment**: Hugging Face Spaces | |
| ## Usage | |
| 1. Select a digit (0-9) from the dropdown | |
| 2. Click "Generate Images" | |
| 3. View 5 unique generated images of the chosen digit | |
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| *This README is uploaded for documentation and evaluation purposes.* | |