| # Handwritten Digit Generator |
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| A web application that generates handwritten digit images using a trained Conditional GAN model. |
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| ## Time Constraints & Future Improvements |
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| 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: |
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| - **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 |
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| ## Current Results |
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| Despite the limited training time, the model successfully generates recognizable digits for all classes (0-9) with reasonable diversity, meeting the contest requirements. |
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| ## Technical Details |
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| - **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 |
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| ## Usage |
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| 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.* |
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