Update README.md

README.md

@@ -85,11 +85,11 @@ pipeline_tag: image-segmentation
 <br>
 
 <div style="border:1px solid black; padding:25px; background-color:#FDFFF4 ; padding-top:10px; padding-bottom:1px;">
-  <h1>FLAIR-INC_rgbie_12cl_resnet34-unet</h1> 
-  <p>The general characteristics of this specific model <strong>FLAIR-INC_rgbie_12cl_resnet34-unet</strong> are :</p>
+  <h1>FLAIR-INC_rgb_12cl_resnet34-unet</h1> 
+  <p>The general characteristics of this specific model <strong>FLAIR-INC_rgb_12cl_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>RGB images (true colours)</li>
     <li>U-Net with a Resnet-34 encoder</li>
     <li>12 class nomenclature : [building, pervious surface, impervious surface, bare soil, water, coniferous, deciduous, brushwood, vineyard, herbaceous, agricultural land, plowed land]</li>
   </ul>
@@ -120,11 +120,6 @@ _**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).
 This model was trained to be coherent withe the FLAIR#1 scientific challenge in which contestants were evaluated of the first 12 classes of the nomenclature. Classes with label greater than 12 were desactivated during training. 
@@ -135,11 +130,11 @@ 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_12cl_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_rgb_12cl_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_12cl_resnet34-unet 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_rgb_12cl_resnet34-unet 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**_ :
@@ -160,7 +155,7 @@ Fine-tuning and prediction tasks are detailed in the README file.
 
 ### Training Data
 
-218 400 patches of 512 x 512 pixels were used to train the **FLAIR-INC_rgbie_12cl_resnet34-unet** model. 
+218 400 patches of 512 x 512 pixels were used to train the **FLAIR-INC_rgb_12cl_resnet34-unet** 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 set (TRAIN or VALIDATION). 
 The following number of patches were used for train and validation :
@@ -185,8 +180,6 @@ Statistics of the TRAIN+VALIDATION set :
 | Red Channel (R)         | 105.08	    |52.17       |
 | 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
@@ -198,8 +191,8 @@ 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]
+  * norm_stds: [52.17, 45.38, 44]
 * Seed: 2022
 * Batch size: 10
 * Number of epochs : 200
@@ -212,7 +205,7 @@ Statistics of the TRAIN+VALIDATION set :
 
 #### Speeds, Sizes, Times
 
-The FLAIR-INC_rgbie_12cl_resnet34-unet model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 
+The FLAIR-INC_rgb_12cl_resnet34-unet model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 
 16 V100 GPUs were used ( 4 nodes, 4 GPUS per node). With this configuration the approximate learning time is 6 minutes per epoch.
 
 FLAIR-INC_rgbie_12cl_resnet34-unet was obtained for num_epoch=84 with corresponding val_loss=0.57. 
@@ -220,9 +213,9 @@ FLAIR-INC_rgbie_12cl_resnet34-unet was obtained for num_epoch=84 with correspond
 
 <div style="position: relative; text-align: center;">
     <p style="margin: 0;">TRAIN loss</p>
-    <img src="FLAIR-INC_rgbie_12cl_resnet34-unet_train-loss.png" alt="TRAIN loss" style="width: 60%; display: block; margin: 0 auto;"/>
+    <img src="FLAIR-INC_rgb_12cl_resnet34-unet_train-loss.png" alt="TRAIN loss" style="width: 60%; display: block; margin: 0 auto;"/>
     <p style="margin: 0;">VALIDATION loss</p>
-    <img src="FLAIR-INC_rgbie_12cl_resnet34-unet_val-loss.png" alt="VALIDATION loss" style="width: 60%; display: block; margin: 0 auto;"/>
+    <img src="FLAIR-INC_rgb_12cl_resnet34-unet_val-loss.png" alt="VALIDATION loss" style="width: 60%; display: block; margin: 0 auto;"/>
 </div>
 
 
@@ -241,7 +234,7 @@ The choice of a separate TEST set instead of cross validation was made to be coh
 
 #### Metrics
 
-With the evaluation protocol, the **FLAIR-INC_rgbie_12cl_resnet34-unet** have been evaluated to **OA=76.509%** and **mIoU=62.716%**. 
+With the evaluation protocol, the **FLAIR-INC_rgb_12cl_resnet34-unet** have been evaluated to **OA=76.509%** and **mIoU=62.716%**. 
 
 The following table give the class-wise metrics :