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license: unknown
language:
- en
pretty_name: Car Part Damage (Parts)
size_categories:
- 1K<n<10K
task_categories:
- image-segmentation
- object-detection
tags:
- car
- car-parts
- supervised
---
# Car Part Damage (Parts)
## Data format
Each example contains:
- `image`: RGB image (Hugging Face `Image` feature; embedded so the viewer works)
- `image_id`: original filename
- `width`: image width in pixels
- `height`: image height in pixels
- `annotations`: list of detection objects (COCO-like), each with:
- `id`: integer object id from the annotation file
- `category`: car part label (string)
- `bbox`: **[x_min, y_min, x_max, y_max]** in absolute pixel coordinates (xyxy)
- `segmentation`: list of polygons, each polygon is a flat list
`[x1, y1, x2, y2, ...]` in pixel coordinates.
`segmentation[0]` is the exterior polygon; subsequent entries are holes if present.
Bounding boxes are derived from the exterior polygon by taking min/max of x and y.
## Splits
- `train`: 948 images
- `test`: 50 images
## Labels
The part classes are:
Back-bumper, Back-door, Back-wheel, Back-window, Back-windshield, Fender, Front-bumper, Front-door,
Front-wheel, Front-window, Grille, Headlight, Hood, License-plate, Mirror, Quarter-panel,
Rocker-panel, Roof, Tail-light, Trunk, Windshield.
## Usage
```python
from datasets import load_dataset
ds = load_dataset("moondream/car_part_damage", split="train")
print(ds[0]["annotations"][0]["bbox"]) # [x_min, y_min, x_max, y_max]
print(ds[0]["annotations"][0]["category"]) # part name
```
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