Update README.md

README.md

@@ -110,70 +110,68 @@ The general characteristics of this specific model **FLAIR-INC_RVBIE_resnet34_un
 ## 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. -->
 
-Although the model can be applied to other type of very high spatial eart observation images, it was initially developed to tackle the problem of classifying aerial images acquired on the French Territory.
-The product called ([BD ORTHO®](https://geoservices.ign.fr/bdortho)) has its own spatial and radiometric specifications. The prediction produced by the model would be all the better as the user task is similar 
-
+Although the model can be applied to other type of very high spatial earth observation images, it was initially developed to tackle the problem of classifying aerial images acquired on the French Territory.
+The product called ([BD ORTHO®](https://geoservices.ign.fr/bdortho)) has its own spatial and radiometric specifications. The model is not intended to be generic to other type of very high spatial resolution images but specific to BD ORTHO images. 
+As a result, the prediction produced by the model would be all the better as the user images are similar to the original ones.
 
 **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**). 
+The BD ORTHO input images are distributed in 8-bit encoding format per channel. When 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)).
+The FLAIR-INC dataset that was used for training is composed of 75 radiometric domains. In the case of aerial images, domain shifts are frequent and are mainly due to : the date of acquisition of the aerial survey (april to november), the spatial domain (equivalent to a french department administrative division) and downstream radimetric processing.
+By construction (sampling 75 domains) the model is robust to these 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(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.
+However 3 classes corresponding to uncertain labelisation (Mixed (16), Ligneous (17) and Other (19)) and 1 class with very poor labelling (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 : (1) should appear at 0 in the logits (2) should never predicted in the Argmax.
 
 
-## Bias, Risks, and Limitations
+<!-- ## Bias, Risks, and Limitations -->
+## Bias, Risks, Limitations and Recommendations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
 **Using the model on input images with other spatial resolution** :
-The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with fixed scale conditions. All patches used for training are derived from aerial images of 0.2 meters spatial resolution. 
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained with fixed scale conditions.All patches used for training are derived from aerial images of 0.2 meters spatial resolution. Only flip and rotate augmentation were performed during the training process.  
 No data augmentation method concerning scale change was used during training. The user should pay attention that generalization issues can occur while applying this model to images that have different spatial resolutions.
 
 **Using the model for other remote sensing sensors** :
-The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained with aerial images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)) that encopass very specific radiometric image processing. 
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained with aerial images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)) that encopass very specific radiometric image processing. 
 Using the model on other type of aerial images or satellite images may imply the use of transfer learning or domain adaptation techniques.
 
 **Using the model on other spatial areas** :
-The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model has been trained on patches reprensenting the French Metropolitan territory. 
+The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on patches reprensenting the French Metropolitan territory. 
 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)}}
-
-
-
-### Recommendations
-
+<!--{{ bias_risks_limitations | default("[More Information Needed]", true)}}-->
+<!--### Recommendations-->
 <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+<!--{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}-->
 
-{{ bias_recommendations | default("Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", true)}}
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
+{{ get_started_code | default("[More Information Needed]", true)}}
 
 
-{{ get_started_code | default("[More Information Needed]", true)}}
 
 ## Training Details
 
 ### Training Data
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-218 400 patchs of 512 x 512 pixels were used to train the model. 
-The train/validation split was performed patchwise to obtain a 80% / 20% distribution. Spatial independancy between patches is guaranted : neighbouring patches are assigned to the same partition :
-* Train set : 174 700 patches
-* Validation set : 43 700 patchs
+218 400 patches of 512 x 512 pixels were used to train the **FLAIR-INC_RVBIE_resnet34_unet_15cl_norm** model. 
+The train/validation split was performed patchwise to obtain a 80% / 20% distribution between train and validation. 
+Annotation was performed at the _zone_ level (~100 patches per _zone_). Spatial independancy between patches is guaranted as patches from the same _zone_ were assigned to the same partition (train or validation). 
+Here are the number of patches used for train and validation :
+| TRAIN set        | 174 700 patches    |
+| VALIDATION set       | 43 700 patchs      |
 
-
-{{ training_data | default("[More Information Needed]", true)}}
+<!--{{ training_data | default("[More Information Needed]", true)}} -->
 
 ### Training Procedure
 
@@ -181,7 +179,9 @@ The train/validation split was performed patchwise to obtain a 80% / 20% distrib
 
 #### Preprocessing [optional]
 
-For traning the model, input normalization was performed so as the input dataset has a mean of 0 and a standart deviation of 1. For this model here are the statistics of the TRAIN+VALIDATION partition. It is recommended that the user apply the same type of input normalization.
+For traning the model, input normalization was performed so as the input dataset has a mean of 0 and a standart deviation of 1 channel wise. 
+For this model here are the statistics of the TRAIN+VALIDATION partition. It is recommended that the user apply the same type of input normalization.
+
 Input normalization was performed 
 | Modalities              | Mean (Train + Validation)       |Std    (Train + Validation)     |
 | ----------------------- | ----------- |----------- |