Football Match Object Detection

Description

This project aims to detect football players in videos using YOLOv5 for training and YOLOv8 for inference. The dataset is sourced from Roboflow.

Dataset

The dataset used for this project is named "football-players-detection" and was exported via roboflow.com on December 5, 2022. It includes 663 images annotated in YOLO v5 PyTorch format. The following pre-processing and augmentation were applied to each image:

• 50% probability of horizontal flip
• Random brightness adjustment of between -20 and +20 percent

You can find the dataset here.

Installation

To get started, install the required packages:

!pip install ultralytics
!pip install roboflow

Usage

Downloading the dataset

from roboflow import Roboflow

rf = Roboflow(api_key="YOUR_API_KEY")
project = rf.workspace("roboflow-jvuqo").project("football-players-detection-3zvbc")
version = project.version(1)
dataset = version.download("yolov5")

Training the Model

!yolo task=detect mode=train model=yolov5s.pt data={dataset.location}/data.yaml epochs=100 imgsz=640

Running Inference

from ultralytics import YOLO

model = YOLO('yolov8s')
results = model.predict('input_videos/08fd33_4.mp4', save=True)

print(results[0])
print('==================================================')
for box in results[0].boxes:
    print(box)

License

This dataset is provided by a Roboflow user under the CC BY 4.0 license.