| --- |
| pretty_name: IGVC Segmentation Dataset |
| tags: |
| - image |
| license: cc-by-4.0 |
| --- |
| |
| # 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 = 62 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 this 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 } |
| } |
| ``` |
