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README.md
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- weather-forecasting
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- diffusion-models
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- rectified-flow
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- meteorology
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- pytorch
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- deep-learning
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license: mit
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datasets:
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- meteolibre
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---
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# MeteoLibre Rectified Flow Model
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In the folder models_shortcut/:
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## Model Description
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- **Model type**: Rectified Flow Diffusion Model
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- **Architecture**: 3D U-Net with FiLM conditioning
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- **Input**: Meteorological data patches (12 channels + 1 lightning channels, 3D spatio-temporal)
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- **Output**: Generated weather forecast data
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- **Training data**: MeteoLibre meteorological dataset
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- **Language(s)**: Python
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- **License**: MIT
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## Training
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The model was trained using:
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- **Framework**: PyTorch with Hugging Face Accelerate
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- **Optimizer**: Adam (lr=5e-4) OR SOAP
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- **Batch size**: 64
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- **Epochs**: 200
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- **Precision**: Mixed precision (bf16)
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- **Distributed training**: Multi-GPU support
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And there is different video exemple for the inference.
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Performance summary for the first wave of shortcut model:
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## Performance Summary
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| Model | Optimizer | Steps | sat_mse | sat_psnr | sat_ssim | light_mae | light_precision | light_recall | light_f1 | light_iou |
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|----------------|-----------|--------|---------|----------|----------|-----------|-----------------|--------------|----------|-----------|
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| RF (Run 1) | - | 128 | 0.0952 | 28.5327 | 0.8042 | 0.0221 | 0.5482 | 0.6535 | 0.5950 | - |
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| RF (Run 2) | - | 128 | 0.1076 | 27.8870 | 0.8000 | 0.0221 | 0.5157 | 0.6454 | 0.5724 | - |
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| Baseline | Persistence| baseline| 0.2368 | 24.5138 | 0.7266 | 0.0154 | 0.6714 | 0.6665 | 0.6678 | 0.1023 |
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| Shortcut | Adam | 16 | 0.0981 | 28.3788 | 0.8106 | 0.0216 | 0.6339 | 0.5192 | 0.5686 | 0.0791 |
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| Shortcut | Adam | 64 | 0.0983 | 28.3702 | 0.8114 | 0.0207 | 0.6609 | 0.5304 | 0.5860 | 0.0791 |
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| Shortcut | Adam | 128 | 0.0983 | 28.3581 | 0.8112 | 0.0208 | 0.6518 | 0.5208 | 0.5769 | 0.0791 |
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| Shortcut | SOAP | 16 | 0.0601 | 30.5008 | 0.8663 | 0.0156 | 0.8654 | 0.6958 | 0.7710 | 0.0818 |
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| Shortcut | SOAP | 64 | 0.0606 | 30.4786 | 0.8661 | 0.0151 | 0.8658 | 0.6879 | 0.7663 | 0.0818 |
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| Shortcut | SOAP | 128 | 0.0605 | 30.4848 | 0.8660 | 0.0151 | 0.8635 | 0.6886 | 0.7656 | 0.0818 |
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Metrics from evaluation on 64x20 elements (satellite and lightning data).
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FCI+radar models summery:
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- radar_finetune_v2_sigma0dot02.safetensors: good model for long term, train with noise on input data (sigma: 0.02)
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- radar_finetune_v3_sigma0dot2.safetensors: train with noise on input data (sigma: 0.2) but in progressive / generative mode (between t = 0./1.)
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