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--- |
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pretty_name: IGVC Segmentation Dataset |
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tags: |
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- image |
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license: cc-by-4.0 |
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--- |
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# IGCV Segmentation Dataset |
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Dataset for training a semantic image segmentation model for the [Intelligent Ground Vehicle Competition](http://www.igvc.org/). |
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## Composition |
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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. |
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**Train** |
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256 frames rendered in 4 different lighting environments using Blender = 1024 images |
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**Test** |
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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 = 62 images |
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## Usage |
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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: |
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```python |
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from torch.utils.data import Dataset |
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from datasets import load_dataset, Dataset as HFDataset |
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import numpy as np |
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class Split: |
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TRAIN = "train" |
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VALID = "valid" |
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TEST = "test" |
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class SegmentationDataset(Dataset): |
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def __init__(self, path="Nico0302/IGVC-Segmentation", split=Split.TRAIN, transform=None, mask_name="obstacle_mask", valid_size=0.125): |
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self.path = path |
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self.split = split |
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self.transform = transform |
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self.mask_name = mask_name |
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self.valid_size = valid_size |
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self.data = self._read_split() |
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def __len__(self): |
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return len(self.data) |
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def __getitem__(self, idx): |
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item = self.data[idx] |
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sample = dict(image=np.array(item["image"]), mask=np.array(item[self.mask_name])) |
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if self.transform is not None: |
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sample = self.transform(**sample) |
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return { |
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"image": np.transpose(sample["image"], (2, 0, 1)), # HWC to CHW (3, H, W) |
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"mask": np.expand_dims(sample["mask"].astype(np.float32) / 255.0, 0), # HW to CHW (1, H, W) |
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} |
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def _read_split(self): |
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dataset = load_dataset(self.path, split="test" if self.split == Split.TEST else "train") |
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assert isinstance(dataset, HFDataset), "Dataset must be a Hugging Face Dataset" |
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if (self.split == Split.TEST): |
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return dataset |
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splits = dataset.train_test_split(test_size=self.valid_size, seed=42) |
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if self.split == Split.VALID: |
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return splits["test"] |
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return splits["train"] |
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``` |
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Using this adapter, the dataset can simple be passed to the [`DataLoader`](https://docs.pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader): |
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```python |
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train_dataset = SegmentationDataset(split=Split.TRAIN) |
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valid_dataset = SegmentationDataset(split=Split.VALID) |
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test_dataset = SegmentationDataset(split=Split.TEST) |
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train_dataloader = DataLoader(train_dataset) |
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valid_dataloader = DataLoader(valid_dataset) |
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test_dataloader = DataLoader(test_dataset) |
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``` |
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## Acknowledgements |
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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. |
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## Citation |
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If you are using this dataset, please cite |
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```bibtex |
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@misc{gres2025IGVC, |
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author = { Nicolas Gres }, |
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title = { IGCV Segmentation Dataset }, |
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year = 2025, |
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url = { https://huggingface.co/datasets/Nico0302/IGVC-Segmentation }, |
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publisher = { Hugging Face } |
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} |
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``` |
