Update README.md

README.md

@@ -79,7 +79,7 @@ pipeline_tag: image-segmentation
 
 # FLAIR model collection
 The FLAIR models is a collection of semantic segmentation models initially developed to classify land cover on very high resolution aerial ortho-images ([BD ORTHO®](https://geoservices.ign.fr/bdortho)). 
-The distributed pre-trained model differ in their :
+The distributed pre-trained models differ in their :
 - input modalities : RVB (true colours), RVBI (true colours + infrared), RVBIE (true colours + infrared + elevation)
 - model architecture : U-Net with a Resnet-34 encoder, Deeplab
 - target class nomenclature : 12 or 15 land cover classes
@@ -93,7 +93,6 @@ The general characteristics of this specific model *FLAIR-INC_RVBIE_resnet34_une
 * 15 class nomenclature [building,pervious_surface,impervious_surface,bare_soil,water,coniferous,deciduous,brushwood,vineyard,herbaceous,agricultural_land,plowed_land,swimming pool,snow,greenhouse]
 
 
-
 ## Model Informations
 
 <!-- Provide the basic links for the model. -->
@@ -106,25 +105,24 @@ The general characteristics of this specific model *FLAIR-INC_RVBIE_resnet34_une
     - hardware: GENCI, XXX
 - **License:** : Apache 2.0
 
-
   
 ## Uses
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
 The model has been trained with
 
-
 **Radiometry of input images** :
 The input images are distributed in 8-bit encoding format per channel. or traning the model, input normalization was performed (see section **Traing Details**). 
 It is recommended that the user apply the same type of input normalization while inferring the model.
 
-
 **Multi-domain model** :
 The FLAIR-INC dataset that was used for training is composed of 75 radiometric domains. In the case of aerial images, domain shifts are due : the date of the aerial survey (april to november), spatial domain (equivalent to a french department administrative division) and downstream radimetric processing.
 By construction the model is robust to theses shifts, and can be applied to any images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)).
 
 **Land Cover classes of prediction** :
-The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes. However 3 classes correspond to 
+The orginial class nomenclature of the FLAIR Dataset is made up of 19 classes(See the [FLAIR dataset](https://huggingface.co/datasets/IGNF/FLAIR) page for details).
+However 3 classes corresponding to uncertain labelisation (Mixed (16), Ligneous (17) and Other (19)) and 1 class with very poor label quantity (Clear cut (15)) were deasctivated during training.
+As a result, the logits produced by the model are of size 19x1, but class 15,16,17 and 19 should appear at 0 in the logits. And labels 15,16,17 and 19 never predicted in the argmax.
 
 
 ## Bias, Risks, and Limitations
@@ -144,8 +142,6 @@ The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained on patches re
 The user should be aware that applying the model to other type of landscapes may imply a drop in model metrics.  
 
 
-
-
 {{ bias_risks_limitations | default("[More Information Needed]", true)}}
 
 
@@ -160,6 +156,7 @@ The user should be aware that applying the model to other type of landscapes may
 
 Use the code below to get started with the model.
 
+
 {{ get_started_code | default("[More Information Needed]", true)}}
 
 ## Training Details
@@ -199,6 +196,38 @@ Input normalization was performed
 
 - **Training regime:** {{ training_regime | default("[More Information Needed]", true)}} <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
+* Model architecture: Unet (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)
+* Seed: 2022
+* Batch size: 10
+* Optimizer : SGD
+* Learning rate : 0.02
+* Class Weights : 
+  * 1: [1, building]
+  * 2: [1, pervious surface]
+  * 3: [1, impervious surface]
+  * 4: [1, bare soil]
+  * 5: [1, water]
+  * 6: [1, coniferous]
+  * 7: [1, deciduous]
+  * 8: [1, brushwood]
+  * 9: [1, vineyard]
+  * 10: [1,herbaceous vegetation]
+  * 11: [1, agricultural land]
+  * 12: [1, plowed land]
+  * 13: [1, swimming_pool]
+  * 14: [1, snow]
+  * 15: [0, clear cut]
+  * 16: [0, mixed]
+  * 17: [0, ligneous]
+  * 18: [1, greenhouse]
+  * 19: [0, other]
+
+
 #### Speeds, Sizes, Times [optional]
 
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->