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---
task_categories:
- object-detection
language:
- en
tags:
- soccer
- football
- player
- referee
- detection
- ball
- ultralytics
- yolov11
- tracking
pretty_name: Soccana_prb_v1
size_categories:
- 10K<n<100K
---
# ⚽ 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.
---
## 📁 Dataset Structure
- ✅ 25,000 images (~1.5GB)
- ✅ Annotations for 3 object classes:
- `player`
- `referee`
- `ball`
- ✅ Data format:
- **Ultralytics YOLO format** (default)
- **COCO JSON format** (included in separate folders)
- ✅ Resolution variety:
- `160x160`, `320x320`, `640x640`, and `1280x1080` (Full HD)
- ✅ Includes:
- **Sliced images** via [SAHI (Slicing Aided Hyper Inference)](https://github.com/obss/sahi)
- **Background-only images**
- **Multi-angle player views**
- **Noisy and occluded samples** for robustness
---
## 🗂️ Folder Structure
```
V1/
├── images/
│ ├── train/
│ └── test/
├── labels/ # YOLO TXT labels
│ ├── train/
│ └── test/
├── coco_test_annotations/ # COCO format labels (train.json, val.json)
├── coco_train_annotations/ # COCO format labels (train.json, val.json)
├── data.yaml # Ultralytics YOLOv8-compatible YAML
└── samples/ # Dataset samples
```
---
## 🧠 Dataset Origin & Processing
- Collected from:
- [SoccerNet](https://www.soccer-net.org/)
- Public match footage
- Game engine data (e.g., FIFA-style renders)
- Augmented with:
- [SAHI](https://github.com/obss/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
---
## 📦 How to Use
Can find detailed guide at [github](https://github.com/Adit-jain/Soccer_Analysis)
## Classes
| Class ID | Label |
| -------- | ------- |
| 0 | Player |
| 1 | Referee |
| 2 | Ball | |