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--- |
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language: |
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- en |
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base_model: |
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- Ultralytics/YOLO11 |
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pipeline_tag: object-detection |
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tags: |
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- soccer |
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- football |
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- player |
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- ball |
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- referee |
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- detection |
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- analysis |
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- ultralytics |
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- pitch |
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datasets: |
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- Adit-jain/Soccana_player_ball_detection_v1 |
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--- |
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# ⚽ SoccerNet Object Detection Model (YOLOv8) |
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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. |
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--- |
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## 🚀 Overview |
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- **Architecture**: YOLOv8n + SAHI (Sliced Aided Hyper Inference) |
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- **Input Size**: 1280x1280 |
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- **Epochs Trained**: 200 |
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- **Batch Size**: 32 |
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- **Classes**: |
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- Player |
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- Referee |
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- Ball |
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- **Formats**: |
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- [✓] YOLOv8 format (Ultralytics) |
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- [✓] COCO format (JSON) |
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--- |
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## Demo |
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SAMPLE LINK : [DRIVE](https://drive.google.com/file/d/1XWEvUuWHv3peKNvTeZiTLyjNtrnYD_RZ/view?usp=sharing) |
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Note : This sample uses Kmeans, UMAP and SIGLIP for team assignment. This does not have Re-identification applied, hence the large player numbers. |
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<p> |
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<img src="Model/thumbnail.jpg" width="600"/> |
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</p> |
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--- |
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## 🧠 Capabilities |
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- **Robust detection** of small objects like the ball across varied resolutions (160x160 to 1280x1080). |
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- Works well on both standard game footage and camera-angled shots. |
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- Can be **paired with**: |
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- ⚽ **KMeans clustering** for team classification (with jersey color embeddings from SIGLIP). |
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- 📈 **Ball interpolation** for smooth ball trajectory tracking. |
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- 🧠 Future integration with ByteTrack or BoT-SORT for player tracking. |
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--- |
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## 📦 Training Details |
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| Parameter | Value | |
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|------------------|------------------| |
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| Epochs | 200 | |
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| Batch Size | 32 | |
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| Image Size | 1280 | |
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| Optimizer | Auto | |
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| Pretrained | True | |
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| Seed | 44 | |
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| Det. Inference | True | |
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| Dropout | 0.3 | |
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| Patience | 100 (early stop) | |
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| iOU Threshold | 0.7 | |
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| Augmentations | RandAugment + Erasing | |
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| AutoAugment | Enabled | |
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| Mosaic | On | |
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| Flip LR | 50% | |
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--- |
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## 🧾 How to Use |
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A detailed guide and code can be found at [github](https://github.com/Adit-jain/Soccer_Analysis) |
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## 🧊 Notes |
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Model supports sliced image inference using SAHI, optimized for high-resolution input. |
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Ideal for sports analytics, heatmaps, player positioning, and advanced tracking systems. |
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