metadata
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
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.