Yolov8n Csgo Player Detection

🚀 keremberke/yolov8n-csgo-player-detection

This is a YOLOv8-based object detection model for detecting CSGO players, leveraging the ultralytics library and trained on the csgo-object-detection dataset.

🚀 Quick Start

✨ Features

• This model supports the detection of CSGO players, with specific labels including ct, cthead, t, and thead.
• It is based on the ultralytics library, which provides a convenient way to perform object detection tasks.

📦 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/yolov8n-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']

Model Performance

More models available at: awesome-yolov8-models