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⚽ Soccer Object Detection Dataset (25K Subset from 1M+ Images)

This dataset is a curated subset (25,000 images) from a larger soccer vision dataset containing over 1 million images (50+ GB). The data was collected and augmented from multiple open-source sources, including the SoccerNet dataset, video game renders, and publicly available match footage.

It is optimized for object detection tasks, especially focusing on soccer-related entities such as players, referees, and the ball, including various augmentation types like background-only and noisy scenes.


πŸ“ Dataset Structure

  • βœ… 25,000 images (~1.5GB)
  • βœ… Annotations for 3 object classes:
    • player
    • referee
    • ball
  • βœ… Data format:
    • Ultralytics YOLO format (default)
    • COCO JSON format (included in separate folders)
  • βœ… Resolution variety:
    • 160x160, 320x320, 640x640, and 1280x1080 (Full HD)
  • βœ… Includes:

πŸ—‚οΈ Folder Structure

V1/
β”œβ”€β”€ images/
β”‚ β”œβ”€β”€ train/
β”‚ └── test/
β”œβ”€β”€ labels/ # YOLO TXT labels
β”‚ β”œβ”€β”€ train/
β”‚ └── test/
β”œβ”€β”€ coco_test_annotations/ # COCO format labels (train.json, val.json)
β”œβ”€β”€ coco_train_annotations/ # COCO format labels (train.json, val.json)
β”œβ”€β”€ data.yaml # Ultralytics YOLOv8-compatible YAML
└── samples/ # Dataset samples

🧠 Dataset Origin & Processing

  • Collected from:
    • SoccerNet
    • Public match footage
    • Game engine data (e.g., FIFA-style renders)
  • Augmented with:
    • SAHI for image slicing
    • Inclusion of background-only and noisy images to reduce false positives and improve generalization
    • Crops and resizes for multi-resolution model training

πŸ“¦ How to Use

Can find detailed guide at github

Classes

Class ID Label
0 Player
1 Referee
2 Ball

Samples

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Models trained or fine-tuned on Adit-jain/Soccana_player_ball_detection_v1

Collection including Adit-jain/Soccana_player_ball_detection_v1