Update README.md

README.md

@@ -62,4 +62,55 @@ configs:
     path: data/val-*
   - split: test
     path: data/test-*
+task_categories:
+- image-segmentation
+language:
+- en
+tags:
+- computer-vision
+- segmentation
+- remote-sensing
+- planetary-data
+pretty_name: mmls-v2
+size_categories:
+- 1K<n<10K
 ---
+
+# MMLS v2 (Multimodal Martian Landslide Dataset)
+
+This dataset contains 128x128 multi-channel images for Martian landslide segmentation. 
+It features 7 data channels (RGB, DEM, Thermal Inertia, Grayscale) and 1 segmentation mask.
+
+## How to use in PyTorch
+
+Because this dataset contains stacked multidimensional arrays, you should use the following wrapper to load it directly into your PyTorch training loops:
+
+```python
+import torch
+from torch.utils.data import DataLoader, Dataset
+from datasets import load_dataset
+
+
+class MMLSHubDataset(Dataset):
+    def __init__(self, split="train"):
+        # load_dataset pulls from the hub; .with_format("torch") auto-converts arrays to Tensors
+        self.hf_dataset = load_dataset("sattwik21/mmls-v2", split=split).with_format("torch")
+        
+    def __len__(self):
+        return len(self.hf_dataset)
+        
+    def __getitem__(self, idx):
+        sample = self.hf_dataset[idx]
+        
+        # Returns a dictionary (or replace with your custom Tensorclass)
+        return {
+            "rgb": sample["rgb"],
+            "dem": sample["dem"],
+            "thermal": sample["thermal_inertial"],
+            "grayscale": sample["grayscale"],
+            "label": sample["label"]
+        }
+
+# Usage:
+train_dataset = MMLSHubDataset(split="train")
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)