Yolov8m Csgo Player Detection

🚀 keremberke/yolov8m-csgo-player-detection

This project is based on the Ultralytics library, using the YOLOv8 model for CSGO player detection, providing high - precision object detection capabilities.

✨ Features

• Object Detection: Specialized in detecting CSGO players, including CTs and Ts, as well as their heads.
• High Precision: Achieved a mAP@0.5(box) of 0.89165 on the validation set of the keremberke/csgo - object - detection dataset.
• Easy to Use: With clear installation and usage instructions, it can be quickly integrated into projects.

📦 Installation

Install ultralyticsplus:

pip install ultralyticsplus==0.0.23 ultralytics==8.0.21

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('keremberke/yolov8m-csgo-player-detection')

# set model parameters
model.overrides['conf'] = 0.25  # NMS confidence threshold
model.overrides['iou'] = 0.45  # NMS IoU threshold
model.overrides['agnostic_nms'] = False  # NMS class-agnostic
model.overrides['max_det'] = 1000  # maximum number of detections per image

# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'

# perform inference
results = model.predict(image)

# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()

📚 Documentation

Supported Labels

['ct', 'cthead', 't', 'thead']

More Models

More models are available at: awesome-yolov8-models

🔧 Technical Details