README.md

metadata

tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - object-detection
  - pytorch
library_name: ultralytics
library_version: 8.0.6
inference: false
datasets:
  - keremberke/forklift-object-detection
model-index:
  - name: keremberke/yolov8n-forklift-detection
    results:
      - task:
          type: object-detection
        dataset:
          type: keremberke/forklift-object-detection
          name: forklift-object-detection
          split: validation
        metrics:
          - type: precision
            value: 0.57081
            name: mAP@0.5(box)

Supported Labels

['forklift', 'person']

How to use

• Install ultralytics and ultralyticsplus:

pip install -U ultralytics ultralyticsplus

• Load model and perform prediction:

from ultralyticsplus import YOLO, render_model_output

# 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
for result in model.predict(image, return_outputs=True):
    print(result["det"]) # [[x1, y1, x2, y2, conf, class]]
    render = render_model_output(model=model, image=image, model_output=result)
    render.show()