Yolov8n Forklift Detection

🚀 keremberke/yolov8n-forklift-detection

This project is a YOLOv8-based object detection model, specifically designed for detecting forklifts and people. It offers high precision and can be easily integrated into various computer vision applications.

🚀 Quick Start

Supported Labels

['forklift', 'person']

How to use

• 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-forklift-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()

More models available at: awesome-yolov8-models

📚 Documentation

Model Information

Evaluation Results

The model has been evaluated on the keremberke/forklift-object-detection dataset's validation split. The evaluation metric used is mAP@0.5(box), and the model achieved a score of 0.83794.