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README.md
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- **Compute infrastructure:**
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- software: python, pytorch-lightning
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- hardware: HPC/AI resources provided by GENCI-IDRIS
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- **License:** :
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---
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#### Training Hyperparameters
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#### Speeds, Sizes, Times
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- **Compute infrastructure:**
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- software: python, pytorch-lightning
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- hardware: HPC/AI resources provided by GENCI-IDRIS
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- **License:** : Etalab 2.0
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---
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#### Training Hyperparameters
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```yaml
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- Model architecture: Unet #(implementation from the [Segmentation Models Pytorch library](https://segmentation-modelspytorch.readthedocs.io/en/latest/docs/api.html#unet))
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- Encoder : Resnet-34 pre-trained with ImageNet
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- Augmentation :
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- VerticalFlip(p=0.5)
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- HorizontalFlip(p=0.5)
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- RandomRotate90(p=0.5)
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- Input normalization (mean=0 | std=1):
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- norm_means: [105.08, 110.87, 101.82]
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- norm_stds: [52.17, 45.38, 44]
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- Seed: 2022
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- Batch size: 10
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- Number of epochs : 200
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- Early stopping : patience 30 and val_loss as monitor criterium
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- Optimizer : SGD
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- Schaeduler : mode = "min", factor = 0.5, patience = 10, cooldown = 4, min_lr = 1e-7
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- Learning rate : 0.02
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- Class Weights : [1-building: 1.0 , 2-pervious surface: 1.0 , 3-impervious surface: 1.0 , 4-bare soil: 1.0 , 5-water: 1.0 , 6-coniferous: 1.0 , 7-deciduous: 1.0 , 8-brushwood: 1.0 , 9-vineyard: 1.0 , 10-herbaceous vegetation: 1.0 , 11-agricultural land: 1.0 , 12-plowed land: 1.0 , 13-swimming_pool: 1.0 , 14-snow: 1.0 , 15-clear cut: 0.0 , 16-mixed: 0.0 , 17-ligneous: 0.0 , 18-greenhouse: 1.0 , 19-other: 0.0]
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```
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#### Speeds, Sizes, Times
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