soccer-player-segmentation – AI Model by lifatsastain | AlphaNeural AI

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lifatsastain/
soccer-player-segmentation

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computer-vision

object-detection

sports-analytics

roboflow-universe-projects/soccer-players-ckbru

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Soccer Player Segmentation (YOLOv8n-seg)

Instance segmentation model for detecting and segmenting soccer players in match footage. Perfomance

media_images_segmentation_results_1_cc2f7ba99917fe599e2f

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

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()