---
language:
- en
base_model:
- Ultralytics/YOLO11
pipeline_tag: object-detection
tags:
- soccer
- football
- player
- ball
- referee
- detection
- analysis
- ultralytics
- pitch
datasets:
- Adit-jain/Soccana_player_ball_detection_v1
---

# ⚽ SoccerNet Object Detection Model (YOLOv8)

This repository hosts a **YOLOv8-based object detection model** trained on a curated and segmented dataset derived from SoccerNet and other public football datasets. The model is designed to detect key entities in a football game — **players**, **referees**, and the **ball** — with high accuracy, even in challenging scenes.

---

## 🚀 Overview

- **Architecture**: YOLOv8n + SAHI (Sliced Aided Hyper Inference)
- **Input Size**: 1280x1280
- **Epochs Trained**: 200
- **Batch Size**: 32
- **Classes**:
  - Player
  - Referee
  - Ball
- **Formats**:
  - [✓] YOLOv8 format (Ultralytics)
  - [✓] COCO format (JSON)

---

## Demo

SAMPLE LINK : [DRIVE](https://drive.google.com/file/d/1XWEvUuWHv3peKNvTeZiTLyjNtrnYD_RZ/view?usp=sharing)

Note : This sample uses Kmeans, UMAP and SIGLIP for team assignment. This does not have Re-identification applied, hence the large player numbers.
<p>
<img src="Model/thumbnail.jpg" width="600"/>
</p>

---

## 🧠 Capabilities

- **Robust detection** of small objects like the ball across varied resolutions (160x160 to 1280x1080).
- Works well on both standard game footage and camera-angled shots.
- Can be **paired with**:
  - ⚽ **KMeans clustering** for team classification (with jersey color embeddings from SIGLIP).
  - 📈 **Ball interpolation** for smooth ball trajectory tracking.
  - 🧠 Future integration with ByteTrack or BoT-SORT for player tracking.

---

## 📦 Training Details

| Parameter         | Value           |
|------------------|------------------|
| Epochs           | 200              |
| Batch Size       | 32               |
| Image Size       | 1280             |
| Optimizer        | Auto             |
| Pretrained       | True             |
| Seed             | 44               |
| Det. Inference   | True             |
| Dropout          | 0.3              |
| Patience         | 100 (early stop) |
| iOU Threshold    | 0.7              |
| Augmentations    | RandAugment + Erasing |
| AutoAugment      | Enabled          |
| Mosaic           | On               |
| Flip LR          | 50%              |

---

## 🧾 How to Use
A detailed guide and code can be found at [github](https://github.com/Adit-jain/Soccer_Analysis)

## 🧊 Notes
Model supports sliced image inference using SAHI, optimized for high-resolution input.
Ideal for sports analytics, heatmaps, player positioning, and advanced tracking systems.