onnx object-detection license:agpl-3.0 region:us
---
license: agpl-3.0
pipeline_tag: object-detection
---
This model is a version of Yolo v8 nano fine-tuned on the freeclimbs v2 dataset to detect climbing holds, particularly holds on home climbing and "spray" walls.  (The dataset is not currently available but I plan to release it in the future.)

It expects a 2560x2560 image (if using the `ultralytics` library as shown below, it will handle this) and detects a single class - climbing holds.

### Usage

```python
from ultralytics import YOLO

model = YOLO("yolov8n-freeclimbs-detect-2.pt")
results = model(
    ["climbing-wall.jpg"],
    imgsz=2560,
    max_det=2000)
``````

### Performance

|           |       |
|-----------|-------|
| Precision | 0.961 |
| Recall    | 0.942 |
| mAP50     | 0.988 |
| mAP50-95  | 0.889 |

(on freeclimbs v2 test set)

### License

Copyright (c) 2024 John LaRocque  
See `LICENSE` for license (AGPL 3). Note that an earlier version of this repository erroneously included an MIT license - since this model was fine-tuned from a model licensed under the AGPL 3, which is incompatible with other licenses, I am not actually able to offer that license.