---
license: apache-2.0
tags:
- computer-vision
- object-detection
- segmentation
- yolov8
- soccer
- sports-analytics
datasets:
- roboflow-universe-projects/soccer-players-ckbru
---

# Soccer Player Segmentation (YOLOv8n-seg)

Instance segmentation model for detecting and segmenting soccer players in match footage.
Perfomance
![media_images_segmentation_results_1_cc2f7ba99917fe599e2f](https://cdn-uploads.huggingface.co/production/uploads/695e701164a518e0880191f5/KtdyEPgnunQsc8VHpGCYy.png)

## Model Details

- **Model type:** YOLOv8n-seg (instance segmentation)
- **Base model:** ultralytics/yolov8n-seg
- **Developed by:** lifatsastain
- **License:** Apache 2.0
- **Training compute:** Kaggle Tesla T4

## Dataset

- **Source:** FIFA World Cup Qatar 2022 — USA vs Netherlands (Dec 3, 2022)
- **Images:** 150 frames extracted from match footage
- **Classes:** Ball, Player, Ref
- **Annotations:** Bounding boxes converted to segmentation masks using SAM (Segment Anything Model)
- **Split:** 103 train / 28 valid / 19 test

## Training

- **Epochs:** 100
- **Image size:** 640
- **Batch size:** 16
- **Optimizer:** AdamW
- **Experiment tracking:** W&B
- The chart
![media_images_training_curves_0_035a970c5f0eb4a3b9ff](https://cdn-uploads.huggingface.co/production/uploads/695e701164a518e0880191f5/dPQBSCdMQdmqGlNtgvrkW.png)


## Evaluation Results

| Metric | Score |
|--------|-------|
| Box mAP50 | 0.919 |
| Box mAP50-95 | 0.725 |
| Mask mAP50 | 0.917 |
| Mask mAP50-95 | 0.539 |
| Precision | 0.945 |
| Recall | 0.886 |

## How to Use

```python
from ultralytics import YOLO

model = YOLO("lifatsastain/soccer-player-segmentation")

results = model("your_image.jpg")
results[0].show()