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README.md

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-# IGCV Segmentation Dataset
+# IGCV Segmentation Dataset
+
+Dataset for training a semantic image segmentation model for the [Intelligent Ground Vehicle Competition](http://www.igvc.org/).
+
+## Composition
+
+Each instance consists of an reference image from the point of view of the robot and the corresponding obstacle (e.g. construction drums, buckets) and lane segmentation masks.
+
+**Train**
+
+256 frames rendered in 4 different lighting environments using Blender = 1024 images 
+
+**Test**
+
+10 frames captured from the [SCR 2023 IGVC run](https://www.youtube.com/watch?v=7tZsk3T3STA) (manually segmented) + 13 frames rendered in 4 lighting environments = 64 images
+
+## Usage
+
+For usage with PyTorch it is recommended to wrap the dataset into a [`Dataset`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) adapter class and generate a training/validation split:
+
+```python
+from torch.utils.data import Dataset
+from datasets import load_dataset, Dataset as HFDataset
+import numpy as np
+
+class Split:
+    TRAIN = "train"
+    VALID = "valid"
+    TEST = "test"
+
+class SegmentationDataset(Dataset):
+    def __init__(self, path="Nico0302/IGVC-Segmentation", split=Split.TRAIN, transform=None, mask_name="obstacle_mask", valid_size=0.125):
+        self.path = path
+        self.split = split
+        self.transform = transform
+        self.mask_name = mask_name
+        self.valid_size = valid_size
+
+        self.data = self._read_split()
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        item = self.data[idx]
+
+        sample = dict(image=np.array(item["image"]), mask=np.array(item[self.mask_name]))
+        if self.transform is not None:
+            sample = self.transform(**sample)
+
+        return {
+            "image": np.transpose(sample["image"], (2, 0, 1)), # HWC to CHW (3, H, W)
+            "mask": np.expand_dims(sample["mask"].astype(np.float32) / 255.0, 0), # HW to CHW (1, H, W)
+        }
+
+    def _read_split(self):
+        dataset = load_dataset(self.path, split="test" if self.split == Split.TEST else "train")
+        assert isinstance(dataset, HFDataset), "Dataset must be a Hugging Face Dataset"
+
+        if (self.split == Split.TEST):
+            return dataset
+        
+        splits = dataset.train_test_split(test_size=self.valid_size, seed=42)
+        if self.split == Split.VALID:
+            return splits["test"]
+        return splits["train"]
+```
+
+Using thi adapter, the dataset can simple be passed to the [`DataLoader`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader):
+
+```python
+train_dataset = SegmentationDataset(split=Split.TRAIN)
+valid_dataset = SegmentationDataset(split=Split.VALID)
+test_dataset = SegmentationDataset(split=Split.TEST)
+
+train_dataloader = DataLoader(train_dataset)
+valid_dataloader = DataLoader(valid_dataset)
+test_dataloader = DataLoader(test_dataset)
+```
+
+## Acknowledgements  
+
+Thank you for [Sooner Competitive Robotics](https://ou.edu/scr/) for allowing me to use frames from their IGVC 2023 run video as part of the test set.
+
+## Citation
+
+If you are using this dataset, please cite
+
+```bibtex
+@misc{gres2025IGVC,
+    author       = { Nicolas Gres },
+    title        = { IGCV Segmentation Dataset },
+    year         = 2025,
+    url          = { https://huggingface.co/datasets/Nico0302/IGVC-Segmentation },
+    publisher    = { Hugging Face }
+}
+```