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README.md

@@ -126,7 +126,7 @@ Project assignments are listed in `split.json` with a proposal of split train/va
 
 The number of points per semantic group across `train` and `test` splits is summarized here:
 
-| Group ID | Train Points | Test Points | Total points | % test/total | Distribution classes in train set | Distribution classes in test set |
+| Group ID | Train Points | Test Points | Total points | % test/total | Distribution classes in train set (%)| Distribution classes in test set (%)|
 |:--------:|:------------:|:-----------:|:------------:|:------------:|:---------------------------------:|:--------------------------------:|
 | 0        | 11'490'104   | 3'859'573   | 15'349'677   | 25.1         | 0.7                               | 0.5                              |
 | 1        | 7'273'270    | 3'223'720   | 10'496'990   | 30.7         | 0.4                               | 0.4                              |
@@ -143,7 +143,7 @@ The number of points per semantic group across `train` and `test` splits is summ
 
 
 The proposed split for train/val repartition:
-| Group ID | Train Points | Val Points  | Total points | % val/total  | Distribution classes in train set | Distribution classes in val set  |
+| Group ID | Train Points | Val Points  | Total points | % val/total  | Distribution classes in train set (%) | Distribution classes in val set (%)  |
 |:--------:|:------------:|:-----------:|:------------:|:------------:|:---------------------------------:|:--------------------------------:|
 | 0        | 8'643'791    | 2'846'313   | 11'490'104   | 24.8         | 0.7                               | 0.7                              |
 | 1        | 5'782'668    | 1'490'602   | 7'273'270    | 20.5         | 0.4                               | 0.4                              |
@@ -160,6 +160,7 @@ The proposed split for train/val repartition:
 
 ### 📈 Class Distribution Visualisation
 
+TODO ABSOLUMENT
 
 ---
 
@@ -173,6 +174,8 @@ from datasets import load_dataset
 dataset = load_dataset("heig-vd-geo/GridNet-HD")
 ```
 
+TODO ABSOLUMENT CODE POUR télécharger et visualiser le dataset
+
 Input/Target Format
 
     Input RGB image: .JPG
@@ -203,6 +206,6 @@ This dataset is released under the CC-BY-4.0 license.
 
 If you use this dataset, please cite the following paper:
 
-    [GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure]
-    [A. Carreaud, S. Li. M. De-Lacour, D. Frinde, J. Skaloud, A. Gressin]
-    In Proceedings of NeurIPS 2025.
+    GridNet-HD: A High-Resolution Multi-Modal Dataset for LiDAR-Image Fusion on Power Line Infrastructure
+    Masked Authors
+    Submitted to NeurIPS 2025.