Yolov8n Visdrone

🚀 mshamrai/yolov8n-visdrone

This project is an object - detection model based on YOLOv8, trained on the VisDrone dataset, which can effectively detect various objects in images.

🚀 Quick Start

The mshamrai/yolov8n-visdrone model is designed for object detection. You can quickly start using it by following the steps below.

✨ Features

• Rich Object Detection: It can detect a variety of common objects, including pedestrians, vehicles, and bicycles.
• High - performance Framework: Based on the Ultralytics framework, ensuring efficient and accurate detection.

📦 Installation

First, you need to install the necessary libraries. You can use the following command to install ultralyticsplus and ultralytics:

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43

💻 Usage Examples

Basic Usage

from ultralyticsplus import YOLO, render_result

# load model
model = YOLO('mshamrai/yolov8n-visdrone')

# 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()

Supported Labels

The model supports the following labels for object detection:

['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']

📄 License

This project uses the OpenRAIL license.

📚 Documentation

Model Information