Update README.md

README.md

@@ -208,7 +208,7 @@ Statistics of the TRAIN+VALIDATION set :
 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. 
+FLAIR-INC_rgbie_12cl_resnet34-unet was obtained for num_epoch=65 with corresponding val_loss=0.55. 
 
 
 <div style="position: relative; text-align: center;">
@@ -234,25 +234,25 @@ The choice of a separate TEST set instead of cross validation was made to be coh
 
 #### Metrics
 
-With the evaluation protocol, the **FLAIR-INC_rgb_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=75.238%** and **mIoU=60.706%**. 
 
 The following table give the class-wise metrics :
 
 | Classes             |   IoU (%)    | Fscore (%)  | Precision (%)  | Recall (%)  |
 | ------------------- | ----------|---------|---------|---------|
-| building            |  82.564  |  90.449  |  90.412  |  90.486  |
-| pervious_surface    |  54.149  |  70.255  |  70.723  |  69.794  |
-| impervious_surface  |  73.807  |  84.930  |  85.848  |  84.032  |
-| bare_soil           |  59.013  |  74.224  |  78.795  |  70.154  |
-| water               |  87.216  |  93.172  |  91.692  |  94.699  |
-| coniferous          |  61.591  |  76.231  |  79.304  |  73.387  |
-| deciduous           |  72.225  |  83.873  |  81.728  |  86.133  |
-| brushwood           |  31.187  |  47.546  |  57.056  |  40.754  |
-| vineyard            |  76.105  |  86.432  |  84.264  |  88.714  |
-| herbaceous          |  51.340  |  67.847  |  70.134  |  65.705  |
-| agricultural_land   |  57.558  |  73.063  |  67.249  |  79.978  |
-| plowed_land         |  45.840  |  62.863  |  61.094  |  64.737  |
-| **average**         |  **62.716**    |  **75.907**  |  **76.525**  |  **75.714**  |
+| building             |  79.007  |  88.273  |  88.892  |  87.662  |
+| pervious_surface     |  53.805  |  69.965  |  69.400  |  70.540  |
+| impervious_surface   |  72.274  |  83.906  |  84.051  |  83.762  |
+| bare_soil            |  61.068  |  75.829  |  74.271  |  77.454  |
+| water                |  83.352  |  90.920  |  90.149  |  91.705  |
+| coniferous           |  55.612  |  71.475  |  76.065  |  67.408  |
+| deciduous            |  69.265  |  81.842  |  77.871  |  86.239  |
+| brushwood            |  28.487  |  44.342  |  59.572  |  35.314  |
+| vineyard             |  75.632  |  86.126  |  84.467  |  87.850  |
+| herbaceous           |  50.723  |  67.306  |  70.726  |  64.202  |
+| agricultural_land    |  56.752  |  72.410  |  66.711  |  79.172  |
+| plowed_land          |  42.490  |  59.639  |  59.501  |  59.777  |
+| **average**         |  **60.706**    |  **74.336**  |  **75.140**  |  **74.257**  |
 
 
 
@@ -265,9 +265,9 @@ The following illustration gives the resulting confusion matrix :
 
 <div style="position: relative; text-align: center;">
     <p style="margin: 0;">Normalized Confusion Matrix (precision)</p>
-    <img src="FLAIR-INC_rgbie_12cl_resnet34-unet_confmat_norm-precision.png" alt="drawing" style="width: 70%; display: block; margin: 0 auto;"/>
+    <img src="FLAIR-INC_rgb_12cl_resnet34-unet_confmat_norm-precision.png" alt="drawing" style="width: 70%; display: block; margin: 0 auto;"/>
     <p style="margin: 0;">Normalized Confusion Matrix (recall)</p>
-    <img src="FLAIR-INC_rgbie_12cl_resnet34-unet_confmat_norm-recall.png" alt="drawing" style="width: 70%; display: block; margin: 0 auto;"/>
+    <img src="FLAIR-INC_rgb_12cl_resnet34-unet_confmat_norm-recall.png" alt="drawing" style="width: 70%; display: block; margin: 0 auto;"/>
 </div>