| # Trucks Damage Dataset | |
| This dataset contains images of trucks with various types of damage annotations. The annotations are in COCO format and include detailed segmentation masks for different types of damage. | |
| ## Dataset Statistics | |
| - Total Images: 795 | |
| - Annotations per category: | |
| - scratch: 1125 | |
| - dent: 745 | |
| - broken part: 278 | |
| - missing part: 374 | |
| - crack: 87 | |
| - lamp broken: 6 | |
| ## Dataset Statistics | |
| - Total Images: 795 | |
| - Annotations per category: | |
| - scratch: 1125 | |
| - dent: 745 | |
| - broken part: 278 | |
| - missing part: 374 | |
| - crack: 87 | |
| - lamp broken: 6 | |
| ## Dataset Description | |
| - Number of Images: 795 | |
| - Format: COCO | |
| - Image Format: JPG | |
| - Annotation Format: JSON | |
| ### Damage Categories: | |
| 1. Scratch | |
| 2. Dent | |
| 3. Missing Part | |
| 4. Broken Part | |
| 5. Flat Tire | |
| 6. Crack | |
| ### Annotation Format | |
| Each annotation includes: | |
| - Category ID | |
| - Segmentation polygons | |
| - Bounding box coordinates [x, y, width, height] | |
| - Area | |
| - Additional metadata | |
| ## Usage | |
| The dataset follows the COCO format and can be used with popular computer vision frameworks like: | |
| - YOLO | |
| - Detectron2 | |
| - MMDetection | |
| - Transformers | |
| ## License | |
| Please specify the license under which this dataset is released. | |