Pool Ball Detection Dataset in YOLO Format

A YOLO-format dataset for object detection tasks. The dataset contains 16 classes representing pool balls numbered 0 through 15. It was created by author nhantu2107 and was last updated on Hugging Face on October 23, 2025.

Use Cases

• Train an object detection model to identify and localize pool balls based on the 16 provided classes.
• Benchmark the performance of YOLO-family models on a constrained, multi-class detection task.
• Develop and test computer vision applications for game analysis or robotic pool players based on the annotated ball images.

Strengths

• Dataset is structured for YOLO, a widely-used object detection framework, with separate train/valid/test splits.
• Contains annotations for 16 distinct object classes (pool balls 0-15).

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

huggingface

Freshness

Last updated 2025-10-23 09:12:37; freshness should be verified.