Yolov8l Visdrone

🚀 mshamrai/yolov8l-visdrone

This project is based on Ultralytics and YOLOv8, aiming to solve object - detection problems in scenarios such as UAV vision. It provides high - precision object - detection capabilities for multiple target types.

🚀 Quick Start

✨ Features

• Object Detection: Support detection of various object types, including pedestrians, vehicles, and bicycles.
• High Precision: Achieved good precision and recall rates on the VisDrone dataset.

📦 Installation

• Install ultralyticsplus:

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/yolov8l-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()

📚 Documentation

Supported Labels

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

🔧 Technical Details

📄 License

This project is licensed under the OpenRAIL license.