Update README.md

README.md

@@ -110,34 +110,34 @@ Point clouds were preprocessed for training with point subsampling, filtering of
 For inference, a preprocessing as close as possible should be used. Refer to the inference configuration file, and to the Myria3D code repository (V3.8).
 
 #### Training Hyperparameters
-* Model architecture: RandLa-Net (implemented with the Pytorch-Geometric framework in [Myria3D](https://github.com/IGNF/myria3d/blob/main/myria3d/models/modules/pyg_randla_net.py))
-* Augmentation :
-  * VerticalFlip(p=0.5)
-  * HorizontalFlip(p=0.5)
-* Features:
-  * Lidar: x, y, z, echo number (1-based numbering), number of echos, reflectance (a.k.a intensity)
-  * Colors:
-    * Original: RGB + Near-Infrared (colorization from aerial images by vertical pixel-point alignement)
-    * Derived: average color = (R+G+B)/3 and NDVI.
-* Input preprocessing:
-  * grid sampling: 0.25 m
-  * random sampling: 40,000 (if higher)
-  * horizontal normalization: mean xy substraction
-  * vertical normalization: min z substraction
-  * coordinates normalization: division by 25 meters
-  * basic occlusion model: nullify color channels if echo_number > 1
-  * features scaling (0-1 range):
-    * echo number and number of echos: division by 7
-    * color (r, g, b, near-infrared, average color): division by 65280 (i.e. 255*256)
-  * features normalization:
-    * reflectance: log-normalization, standardization, clipping of amplitude above 3 standard deviations.
-    * average color: same as reflectance. 
-* Batch size: 10
-* Number of epochs : 100 (min) - 150 (max)
-* Early stopping : patience 6 and val_loss as monitor criterium
-* Optimizer : Adam
-* Schaeduler : mode = "min", factor = 0.5, patience = 20, cooldown = 5
-* Learning rate : 0.004
+- Model architecture: RandLa-Net (implemented with the Pytorch-Geometric framework in [Myria3D](https://github.com/IGNF/myria3d/blob/main/myria3d/models/modules/pyg_randla_net.py))
+- Augmentation :
+  - VerticalFlip(p=0.5)
+  - HorizontalFlip(p=0.5)
+- Features:
+  - Lidar: x, y, z, echo number (1-based numbering), number of echos, reflectance (a.k.a intensity)
+  - Colors:
+    - Original: RGB + Near-Infrared (colorization from aerial images by vertical pixel-point alignement)
+    - Derived: average color = (R+G+B)/3 and NDVI.
+- Input preprocessing:
+  - grid sampling: 0.25 m
+  - random sampling: 40,000 (if higher)
+  - horizontal normalization: mean xy substraction
+  - vertical normalization: min z substraction
+  - coordinates normalization: division by 25 meters
+  - basic occlusion model: nullify color channels if echo_number > 1
+  - features scaling (0-1 range):
+    - echo number and number of echos: division by 7
+    - color (r, g, b, near-infrared, average color): division by 65280 (i.e. 255*256)
+  - features normalization:
+    - reflectance: log-normalization, standardization, clipping of amplitude above 3 standard deviations.
+    - average color: same as reflectance. 
+- Batch size: 10 (x 6 GPUs)
+- Number of epochs : 100 (min) - 150 (max)
+- Early stopping : patience 6 and val_loss as monitor criterium
+- Optimizer : Adam
+- Schaeduler : mode = "min", factor = 0.5, patience = 20, cooldown = 5
+- Learning rate : 0.004
 
 #### Speeds, Sizes, Times
 
@@ -183,7 +183,7 @@ The following illustration gives the resulting confusion matrix :
 
 ### Results
 
-From test patches with at least 10k points (i.e. at least 4 pts/m²), we sample without cherry-picking, 
+From test patches with at least 10k points (i.e. at least 4 pts/m²), we sample patches without cherry-picking, 
 to match matches with the following metadata: a) URBAN, b) WATER & BRIDGE, c) OTHER_PARKING, d) BUILD_GREENHOUSE, e) HIGHSLOPE.
 
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