---
license: apache-2.0
task_categories:
- keypoint-detection
- object-detection
tags:
- yolo
- yolov8-pose
- snooker
- table-detection
- calibration
- computer-vision
library_name: ultralytics
---

# SCOS Snooker Table Corner Detector

Detects the **4 corners of a snooker table** (TL, TR, BR, BL) for automatic
perspective calibration in the SCOS (Snooker Club Operating System).

Replaces manual click-to-calibrate with a single model inference call.

## Model Details
- **Architecture**: YOLOv8s-pose (keypoint detection)
- **Keypoints**: 4 (Top-Left, Top-Right, Bottom-Right, Bottom-Left)
- **Image size**: 640x640
- **Class**: `table` (1 class)

## Usage

```python
from ultralytics import YOLO

model = YOLO('asadahsan148/scos-corner-detector')  # auto-download from HF Hub
results = model('frame.jpg')

# Get corners: [TL, TR, BR, BL]
corners = results[0].keypoints.xy[0].cpu().numpy()
# corners[0] = TL, corners[1] = TR, corners[2] = BR, corners[3] = BL
```

## SCOS Integration

The SCOS backend auto-calibration route calls the HF Space inference endpoint,
which runs this model and returns the 4 corner coordinates directly.
These are fed into the existing perspective warp pipeline without any manual input.

## Training Data
Annotated frames extracted from live CCTV footage of snooker tables.
Labels: 4 keypoints per frame in YOLO pose format.