Update README.md

README.md

@@ -108,7 +108,7 @@ pipeline_tag: image-segmentation
 - **Compute infrastructure:** 
     - software: python, pytorch-lightning
     - hardware: HPC/AI resources provided by GENCI-IDRIS
-- **License:** : Apache 2.0
+- **License:** : Etalab 2.0
 
 ---
 
@@ -190,24 +190,25 @@ Statistics of the TRAIN+VALIDATION set :
 
 #### Training Hyperparameters
 
-* Model architecture: FPN (implementation from the [Segmentation Models Pytorch library](https://segmentation-modelspytorch.readthedocs.io/en/latest/docs/api.html#unet))
-* Encoder : Resnet-34 pre-trained with ImageNet
-* Augmentation :
-  * VerticalFlip(p=0.5)
-  * HorizontalFlip(p=0.5)
-  * RandomRotate90(p=0.5)
-* Input normalization (mean=0 | std=1):
-  * norm_means: [105.08, 110.87, 101.82, 106.38, 53.26]
-  * norm_stds: [52.17, 45.38, 44, 39.69, 79.3]
-* Seed: 2022
-* Batch size: 10
-* Number of epochs : 200
-* Early stopping : patience 30 and val_loss as monitor criterium 
-* Optimizer : SGD
-* Schaeduler : mode = "min", factor = 0.5, patience = 10, cooldown = 4, min_lr = 1e-7
-* Learning rate : 0.02
-* 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]
-
+```yaml
+- Model architecture: FPN #(implementation from the [Segmentation Models Pytorch library](https://segmentation-modelspytorch.readthedocs.io/en/latest/docs/api.html#unet))
+- Encoder : Resnet-34 pre-trained with ImageNet
+- Augmentation :
+  - VerticalFlip(p=0.5)
+  - HorizontalFlip(p=0.5)
+  - RandomRotate90(p=0.5)
+- Input normalization (mean=0 | std=1):
+  - norm_means: [105.08, 110.87, 101.82, 106.38, 53.26]
+  - norm_stds: [52.17, 45.38, 44, 39.69, 79.3]
+- Seed: 2022
+- Batch size: 10
+- Number of epochs : 200
+- Early stopping : patience 30 and val_loss as monitor criterium 
+- Optimizer : SGD
+- Schaeduler : mode = "min", factor = 0.5, patience = 10, cooldown = 4, min_lr = 1e-7
+- Learning rate : 0.02
+- 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]
+```
 
 #### Speeds, Sizes, Times