yolov8n-visdrone / README.md
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metadata
tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - object-detection
  - pytorch
  - visdrone
  - uav
library_name: ultralytics
library_version: 8.0.43
inference: false
model-index:
  - name: mshamrai/yolov8n-visdrone
    results:
      - task:
          type: object-detection
        metrics:
          - type: precision
            value: 0.34094
            name: mAP@0.5(box)
license: openrail
mshamrai/yolov8n-visdrone

Supported Labels

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

How to use

pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
  • Load model and perform prediction:
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()