README.md

---
library_name: ultralytics
tags:
  - object-detection
  - yolo
  - ultralytics
license: mit
---

# perp-cnn — Bowtip Detection

Detects the bow tip of boats in photofinish images. Used in [perp_web](https://github.com/tillsc/perp_web) for automated boat race timing.

## Usage

```python
import numpy as np
from perp_cnn import predict

image: np.ndarray = ...  # H×W×3, BGR or RGB
results = predict(image)

for box in results[0].boxes:
    x1, y1, x2, y2 = map(int, box.xyxy[0])
    conf = float(box.conf[0])
```

Or install as a Git dependency via Poetry:

```toml
perp-cnn = { git = "https://github.com/tillsc/perp_cnn.git" }
```

## Model

- Architecture: YOLO11n
- Input: arbitrary image size (resized to 640px internally)
- Output: bounding boxes for class `bowtip`
- Training data: photofinish images from rowing races

## Performance

| | |
|---|---|
| Precision | 100% |
| Recall | 97.9% |
| Box accuracy (mAP50) | 99.5% |
| Box precision (mAP50-95) | 74.6% |
| CPU inference | ~19ms / ~52 FPS |

## Training Notes

- No horizontal or vertical flips (boat orientation matters)
- No rotation or perspective warp
- Slight translation and scaling augmentation