Update README.md

README.md

@@ -92,10 +92,10 @@ pipeline_tag: image-segmentation
 
 <div style="border:1px solid black; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;">
   <h1>FLAIR-INC_rgbie_15cl_resnet34-unet</h1> 
-  <p>The general characteristics of this specific model <strong>FLAIR-INC_rgbie_15cl_resnet34-unet</strong> are :</p>
+  <p>The general characteristics of this specific model <strong>FLAIR-INC_rgbi_15cl_resnet34-unet</strong> are :</p>
   <ul style="list-style-type:disc;">
     <li>Trained with the FLAIR-INC dataset</li>
-    <li>RGBIE images (true colours + infrared + elevation)</li>
+    <li>RGBI images (true colours + infrared )</li>
     <li>U-Net with a Resnet-34 encoder</li>
     <li>15 class nomenclature : [building, pervious surface, impervious surface, bare soil, water, coniferous, deciduous, brushwood, vineyard, herbaceous, agricultural land, plowed land, swimming pool, snow, greenhouse]</li>
   </ul>
@@ -126,11 +126,6 @@ _**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 frequent and are mainly due to : the date of acquisition of the aerial survey (from april to november), the spatial domain (equivalent to a french department administrative division) and downstream radiometric 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)).
 
-_**Specification for the Elevation channel**_ :
-The fifth dimension of the RGBIE images is the Elevation (height of building and vegetation). This information is encoded in a 8-bit encoding format. 
-When decoded to [0,255] ints, a difference of 1 should coresponds to 0.2 meters step of elevation difference.   
-
-
 _**Land Cover classes of prediction**_ :
 The orginial class nomenclature of the FLAIR Dataset encompasses 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 labelling (Clear cut (15)) were desactivated during training.
@@ -141,15 +136,15 @@ As a result, the logits produced by the model are of size 19x1, but classes n°
 ## Bias, Risks, Limitations and Recommendations
 
 _**Using the model on input images with other spatial resolution**_ :
-The FLAIR-INC_rgbie_15cl_resnet34-unet model was trained with fixed scale conditions. All patches used for training are derived from aerial images with 0.2 meters spatial resolution. Only flip and rotate augmentations were performed during the training process.  
+The FLAIR-INC_rgbi_15cl_resnet34-unet model was trained with fixed scale conditions. All patches used for training are derived from aerial images with 0.2 meters spatial resolution. Only flip and rotate augmentations 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_rgbie_15cl_resnet34-unet model was trained with aerial images of the ([BD ORTHO® product](https://geoservices.ign.fr/bdortho)) that encopass very specific radiometric image processing. 
+The FLAIR-INC_rgbi_15cl_resnet34-unet 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_rgbie_15cl_resnet34-unet model was trained on patches reprensenting the French Metropolitan territory. 
+The FFLAIR-INC_rgbi_15cl_resnet34-unet 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.  
 
 ---
@@ -192,7 +187,6 @@ Statistics of the TRAIN+VALIDATION set :
 | Green Channel (G)       | 110.87      |45.38       |
 | Blue Channel (B)        | 101.82	    |44.00       |
 | Infrared Channel (I)    | 106.38	    |39.69       |
-| Elevation Channel (E)   | 53.26       |79.30       |
 
 
 #### Training Hyperparameters
@@ -204,8 +198,8 @@ Statistics of the TRAIN+VALIDATION set :
   * 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]
+  * norm_means: [105.08, 110.87, 101.82, 106.38]
+  * norm_stds: [52.17, 45.38, 44, 39.69]
 * Seed: 2022
 * Batch size: 10
 * Number of epochs : 200