Football Player Detection Yolo8
This repository contains a YOLO object detection model fine-tuned for football (soccer) match analysis. The model localizes football players in broadcast or tactical camera angles.
Fuente del modelo
- Tipo
- Other
- Tamaño del archivo
- 5,97 MB
- Versión
- vRevision e01334d
- Actualizado
- 10 oct 2026
- Descargas
- 29
- Fuente
- Hugging Face
Descripción de la fuente
Football Player Detection (YOLO)
Ultralytics YOLO PyTorch License: MIT
1. Overview
This repository contains a YOLO object detection model fine-tuned for football (soccer) match analysis. The model localizes football players in broadcast or tactical camera angles.
2. Model Details
- Architecture: YOLOv8 Nano (
yolov8n.pt) - Task: Object Detection
- Framework: PyTorch / Ultralytics
- Input Resolution: 640x640
- Epochs Trained: 100
3. Dataset
- Format: YOLO txt annotations
- Image Resolution: Resized to 640 pixels on the longest edge during training.
- Augmentations: Mosaic (
1.0), left-right flips (0.5), scaling (0.5), translation (0.1).
4. Training Configuration
Key hyperparameters extracted from args.yaml:
- Epochs: 100
- Batch Size: 16
- Image Size: 640
- Optimizer: Auto
- Initial Learning Rate (
lr0): 0.01 - Final Learning Rate Fraction (
lrf): 0.01 - Momentum: 0.937
- Weight Decay: 0.0005
- NMS IoU Threshold: 0.7
5. Performance
Evaluation metrics at epoch 100:
- Precision: 0.799
- Recall: 0.752
- mAP50: 0.711
- mAP50-95: 0.443
6. Training Visualizations
Training Results
Training progression showing box, class, and DFL losses alongside precision, recall, and mAP metrics over 100 epochs.
Normalized Confusion Matrix
Normalized confusion matrix evaluating true positive predictions and background false negatives.
Precision-Recall Curve
Precision-Recall (PR) curve demonstrating the trade-off between precision and recall across confidence thresholds.
F1 Curve
F1-score curve illustrating optimal confidence thresholds.
7. Installation
pip install ultralytics torch torchvision
8. Usage
Python API
from ultralytics import YOLO
model = YOLO("weights/best.pt")
results = model("football.jpg")
results[0].show()
CLI
yolo task=detect mode=predict model=weights/best.pt source=football.jpg
9. Repository Structure
.
├── weights/
│ ├── best.pt
│ └── last.pt
├── args.yaml
├── results.csv
├── results.png
├── confusion_matrix.png
├── confusion_matrix_normalized.png
├── BoxPR_curve.png
├── BoxP_curve.png
├── BoxR_curve.png
├── BoxF1_curve.png
├── labels.jpg
├── val_batch0_pred.jpg
├── val_batch1_pred.jpg
└── val_batch2_pred.jpg
Fuentes
1 fuente
Fuentes
1 fuenteVerificado 10 oct
Artefactos del modelo
1 artefacto
Artefactos del modelo
1 artefactoExtractos de fuentes
3 extractos
Extractos de fuentes
3 extractos--- language: - en license: mit pipeline_tag: object-detection library_name: ultralytics tags: - yolo11 - football - object-detection - pytorch - ultralytics metrics: - precision - recall - map50 - map50-95 --- # Football Player Detection (YOLO) [](https://github.com/ultralytics/ultralytics) [](https://pytorch.org/) [](https://opensource.org/licenses/MIT) ## 1. Overview This repository contains a YOLO object detection model fine-tuned for football (soccer) match analysis. The model localizes football players in broadcast or tactical camera angles. ## 2. Model Details - **Architecture:** YOLOv8 Nano (`yolov8n.pt`) - **Task:** Object Detection - **Framework:** PyTorch / Ultralytics - **Input Resolution:** 640x640 - **Epochs Trained:** 100 ## 3. Dataset - **Format:** YOLO txt annotations - **Image Resolution:** Resized to 640 pixels on the longest edge during training. - **Augmentations:** Mosaic (`1.0`), left-right flips (`0.5`), scaling (`0.5`), translation (`0.1`). ## 4. Training Configuration Key hyperparameters extracted from `args.yaml`: - **Epochs:** 100 - **Batch Size:** 16 - **Image Size:** 640 - **Optimizer:** Auto - **Initial Learning Rate (`lr0`):** 0.01 - **Final Learning Rate Fraction (`lrf`):** 0.01 - **Momentum:** 0.937 - **Weight Decay:** 0.0005 - **NMS IoU Threshold:** 0.7 ## 5. Performance Evaluation metrics at epoch 100: - **Precision:** 0.799 - **Recall:** 0.752 - **mAP50:** 0.711 - **mAP50-95:** 0.443 ## 6. Training Visualizations  *Training progression showing box, class, and DFL losses alongside precision, recall, and mAP metrics over 100 epochs.*  *Normalized confusion matrix evaluating true positive predictions and background false negatives.*  *Precision-Recall (PR) curve demonstrating the trade-off between precision and recall across confidence thresholds.*  *F1-score curve illustrating optimal confidence thresholds.* ## 7. Installation ```bash pip install ultralytics torch torchvision ``` ## 8. Usage ### Python...
Source context: 29 downloads · 1 likes · Pipeline object-detection · Library ultralytics · Repo ouss100/football-player-detection-yolo8
Source context: 121 downloads · 0 likes · Pipeline object-detection · Library ultralytics · Repo ouss100/football-player-detection-yolo8
