Update README.md

README.md

@@ -91,8 +91,8 @@ pipeline_tag: image-segmentation
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-  <h1>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model</h1> 
-  <p>The general characteristics of this specific model <strong>FLAIR-INC_RVBIE_resnet34_unet_15cl_norm</strong> are :</p>
+  <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>
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     <li>Trained with the FLAIR-INC dataset</li>
     <li>RGBIE images (true colours + infrared + elevation)</li>
@@ -141,15 +141,15 @@ 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_RVBIE_resnet34_unet_15cl_norm 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_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.  
 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 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_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. 
 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 was trained on patches reprensenting the French Metropolitan territory. 
+The FLAIR-INC_rgbie_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.  
 
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@@ -218,10 +218,10 @@ Statistics of the TRAIN+VALIDATION set :
 
 #### Speeds, Sizes, Times
 
-The FLAIR-INC_RVBIE_resnet34_unet_15cl_norm model was trained on a HPC/AI resources provided by GENCI-IDRIS (Grant 2022-A0131013803). 
+The FLAIR-INC_rgbie_15cl_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_RVBIE_resnet34_unet_15cl_norm was obtained for num_epoch=76 with corresponding val_loss=0.56. 
+FLAIR-INC_rgbie_15cl_resnet34-unet was obtained for num_epoch=76 with corresponding val_loss=0.56. 
 
 
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