Add comprehensive Google Colab training notebook for SARATR-X experiments#1
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Add comprehensive Google Colab training notebook for SARATR-X experiments#1
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…ents This commit adds a complete Google Colab notebook that enables training the SARATR-X model with 4 different reconstruction techniques on Sentinel-1&2 dataset. Features: - Optimized for Google Colab free tier (T4 GPU, 1.5h sessions) - Automatic Sentinel-1&2 dataset download from Kaggle - PNG to NPY conversion for faster loading - 4 experiment configurations: 1. Pixel-reconstruction (SAR → SAR) 2. MGF-reconstruction (SAR → Multi-scale Gradient Features) 3. RGB-reconstruction (SAR → RGB optical) 4. Greyscale-reconstruction (SAR → Greyscale optical) - Google Drive integration for persistent checkpoint storage - Automatic checkpointing every 5 epochs - TensorBoard logging and visualization - Resume training capability for session expiry handling - Comprehensive README with setup instructions and troubleshooting Training optimizations: - Batch size: 32 (reduced for T4 GPU memory) - Mixed precision (16-bit) training - 50 epochs per experiment (~15-20 min each) - Custom data loaders for SAR-to-SAR and greyscale conversion Files added: - notebook/colab_training_experiments.ipynb: Main Colab notebook - notebook/COLAB_EXPERIMENTS_README.md: Detailed usage instructions
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This commit adds a complete Google Colab notebook that enables training the SARATR-X model with 4 different reconstruction techniques on Sentinel-1&2 dataset.
Features:
Training optimizations:
Files added: