| --- |
| license: cc-by-nd-4.0 |
| pretty_name: "A2D2" |
| task_categories: |
| - image-segmentation |
| tags: |
| - computer-vision |
| - road |
| - car |
| - crosswalk |
| - truck |
| - sidewalk |
| - traffic-light |
| - bus |
| - bridge |
| - stop-sign |
| - sign |
| - pictograph |
| annotations_creators: |
| - expert-generated |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "train/**/*" |
| --- |
| |
| [](https://pictograph.io/datasets/pictograph-research/a2d2) |
|
|
| [](https://pictograph.io/datasets/pictograph-research/a2d2)   [](https://creativecommons.org/licenses/by-nd/4.0/) |
|
|
| **[View on Pictograph](https://pictograph.io/datasets/pictograph-research/a2d2)** · [Pictograph Research](https://pictograph.io/org/pictograph-research) · [Creative Commons Attribution-NoDerivatives 4.0](https://creativecommons.org/licenses/by-nd/4.0/) |
|
|
| ## About |
|
|
| **A2D2** is a computer-vision dataset from [A2D2](https://a2d2-dataset.github.io), hosted on [Pictograph](https://pictograph.io/datasets/pictograph-research/a2d2). Instance segmentation of road users from the Audi Autonomous Driving Dataset (A2D2): 38,554 frames from the six cameras of an Audi Q7 e-tron, with every car, pedestrian, truck, bicycle, small vehicle, utility vehicle and tractor outlined as a polygon. The most common detected objects are road, car, crosswalk, truck, sidewalk, traffic light. On Pictograph you can browse every annotated image, import it into your own workspace in one click, export it in a dozen formats, or train a model on it directly. |
|
|
| ## At a glance |
|
|
| | Metric | Value | |
| | :--- | ---: | |
| | Images | 38,554 | |
| | Annotations | 549,965 | |
| | Classes | 7 | |
| | Annotation types | polygon | |
| | Splits | train | |
|
|
| ## Quick start |
|
|
| Load it in one line with the `datasets` library, then read each record's boxes and class names: |
|
|
| ```python |
| from datasets import load_dataset |
| from PIL import ImageDraw |
| |
| ds = load_dataset("pictograph/a2d2", split="train") |
| example = ds[0] |
| image = example["image"] # a PIL image |
| objects = example["objects"] # {bbox, categories, category_names} |
| |
| # draw every bounding box with its class name |
| draw = ImageDraw.Draw(image) |
| for (x, y, w, h), name in zip(objects["bbox"], objects["category_names"]): |
| draw.rectangle([x, y, x + w, y + h], outline="red", width=3) |
| draw.text((x, y - 12), name, fill="red") |
| |
| # polygon masks live under example["segmentation"]: |
| # [{"label": name, "category": idx, "points": [[x, y], ...]}, ...] |
| image.show() |
| ``` |
|
|
| Prefer a full annotation editor, one-click import, multi-format export, and one-click training? [Open this dataset on Pictograph](https://pictograph.io/datasets/pictograph-research/a2d2). |
|
|
| ## Dataset structure |
|
|
| This dataset uses the Hugging Face `imagefolder` layout: each split directory holds the images plus a `metadata.jsonl` that links every image to its annotations by `file_name`. |
|
|
| | Field | Description | |
| | :--- | :--- | |
| | `file_name` | Path to the image within the split directory. | |
| | `objects.bbox` | Bounding boxes as `[x, y, width, height]` (pixels). | |
| | `objects.categories` | Integer class index per box (matches the class list below). | |
| | `objects.category_names` | Human class name per box. | |
| | `segmentation` | List of `{label, category, points}`; `points` is a polygon ring `[[x, y], ...]`. | |
|
|
| ## Data instance |
|
|
| One record (bounding boxes are `[x, y, width, height]` in pixels; the class index maps into the class list below): |
|
|
| ```python |
| { |
| "image": <PIL.Image (RGB)>, |
| "objects": { |
| "bbox": [[172.0, 192.0, 249.4, 152.7]], |
| "categories": [0], |
| "category_names": ["car"] |
| }, |
| "segmentation": [ |
| {"label": "car", "category": 0, "points": [[176.9, 207.2], [259.0, 274.1], ...]} |
| ] |
| } |
| ``` |
|
|
| ## Classes |
|
|
| Class index matches `objects.categories` in `metadata.jsonl`. |
|
|
| | # | Class | Annotations | |
| | ---: | :--- | ---: | |
| | 0 | car | 372,389 | |
| | 1 | pedestrian | 58,401 | |
| | 2 | truck | 67,059 | |
| | 3 | small vehicle | 7,812 | |
| | 4 | utility vehicle | 4,057 | |
| | 5 | bicycle | 40,136 | |
| | 6 | tractor | 111 | |
|
|
| ## License |
|
|
| Released under [Creative Commons Attribution-NoDerivatives 4.0](https://creativecommons.org/licenses/by-nd/4.0/). When you use this data, please credit **Audi AG (Geyer et al., 2020)**. |
|
|
| ## Source and attribution |
|
|
| The images and annotations come from [A2D2](https://a2d2-dataset.github.io), created by Audi AG (Geyer et al., 2020) and released under Creative Commons Attribution-NoDerivatives 4.0. We are grateful to the original authors. If you use this data, please cite the original source above. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{geyer2020a2d2, |
| title = {{A2D2}: {Audi Autonomous Driving Dataset}}, |
| author = {Geyer, Jakob and Kassahun, Yohannes and Mahmudi, Mentar and Ricou, Xavier and Durgesh, Rupesh and Chung, Andrew S. and Hauswald, Lorenz and Pham, Viet Hoang and M{\"u}hlegg, Maximilian and Dorn, Sebastian and Fernandez, Tiffany and J{\"a}nicke, Martin and Mirashi, Sudesh and Savani, Chiragkumar and Sturm, Martin and Vorobiov, Oleksandr and Oelker, Martin and Garreis, Sebastian and Schuberth, Peter}, |
| journal = {arXiv preprint arXiv:2004.06320}, |
| year = {2020} |
| } |
| ``` |
|
|
| --- |
|
|
| *Published from [Pictograph](https://pictograph.io/datasets/pictograph-research/a2d2) - annotate, train, and deploy from one API.* |
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