Update README.md

README.md

@@ -140,6 +140,7 @@ dataset = build_terramesh_dataset(
     path="https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",  # Streaming or local path
     modalities=["S2L2A"], 
     split="val",
+    shuffle=False,  # Set false for split="val"
     batch_size=8
 )
 # Batch keys: ["__key__", "__url__", "image"]
@@ -148,6 +149,7 @@ dataset = build_terramesh_dataset(
 dataset = build_terramesh_dataset(
     path="https://huggingface.co/datasets/ibm-esa-geospatial/TerraMesh/resolve/main/",  # Streaming or local path
     modalities=["S2L2A", "S2L1C", "S2RGB", "S1GRD", "S1RTC", "DEM", "NDVI", "LULC"], 
+    shuffle=False,  # Set false for split="val"
     split="val",
     batch_size=8
 )
@@ -176,7 +178,7 @@ However, it requires some wrapping to bring the data into the expected shape.
 ```python
 import albumentations as A
 from albumentations.pytorch import ToTensorV2
-from terramesh import build_terramesh_dataset, Transpose, MultimodalTransforms
+from terramesh import build_terramesh_dataset, Transpose, MultimodalTransforms, MultimodalNormalize, statistics
 
 # Define all image modalities
 modalities = ["S2L2A", "S2L1C", "S2RGB", "S1GRD", "S1RTC", "DEM", "NDVI", "LULC"]
@@ -185,6 +187,7 @@ modalities = ["S2L2A", "S2L1C", "S2RGB", "S1GRD", "S1RTC", "DEM", "NDVI", "LULC"
 val_transform = MultimodalTransforms(
     transforms=A.Compose([  # We use albumentations because of the shared transform between image modalities
         Transpose([1, 2, 0]),  # Convert data to channel last (expected shape from albumentations)
+        MultimodalNormalize(mean=statistics["mean"], std=statistics["std"]),
         A.CenterCrop(224, 224),  # Use center crop in val split
         # A.RandomCrop(224, 224),  # Use random crop in train split
         # A.D4(),  # Optionally, use random flipping and rotation for the train split
@@ -205,7 +208,14 @@ dataset = build_terramesh_dataset(
 )
 ```
 
-If you only use a single modality, you don't need to specify `additional_targets`. 
+If you only use a single modality, you don't need to specify `additional_targets`. You need to change the normalization to:
+```
+        MultimodalNormalize(
+            mean={"image": statistics["mean"]["<modality>"]},
+            std={"image": statistics["std"]["<modality>"]}
+        ),
+```
+
 
 ### Returning metadata