| # Ultralytics YOLO 🚀, AGPL-3.0 license | |
| # DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University | |
| # Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/ | |
| # Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml | |
| # parent | |
| # ├── ultralytics | |
| # └── datasets | |
| # └── dota1 ← downloads here (2GB) | |
| # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] | |
| path: ../datasets/DOTAv1 # dataset root dir | |
| train: images/train # train images (relative to 'path') 1411 images | |
| val: images/val # val images (relative to 'path') 458 images | |
| test: images/test # test images (optional) 937 images | |
| # Classes for DOTA 1.0 | |
| names: | |
| 0: plane | |
| 1: ship | |
| 2: storage tank | |
| 3: baseball diamond | |
| 4: tennis court | |
| 5: basketball court | |
| 6: ground track field | |
| 7: harbor | |
| 8: bridge | |
| 9: large vehicle | |
| 10: small vehicle | |
| 11: helicopter | |
| 12: roundabout | |
| 13: soccer ball field | |
| 14: swimming pool | |
| # Download script/URL (optional) | |
| download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.zip | |