readme:  
  ---
  tags:
  - ultralyticsplus
  - ultralytics
  - yolov8
  - yolo
  - vision
  - object-detection
  - pytorch
  library_name: ultralytics
  library_version: 8.0.4
  inference: false
  
  model-index:
  - name: ultralyticsplus/yolov8s
    results:
    - task:
        type: object-detection
  
      metrics:
        - type: precision  # since mAP is not available on hf.co/metrics
          value: 0.449  # min: 0.0 - max: 1.0
          name: mAP
  ---
  
  ### Supported Labels
  
  ```
  ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']
  ```
  
  
  ### How to use
  
  - Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
  
  ```bash
  pip install -U ultralyticsplus==0.0.14
  ```
  
  - Load model and perform prediction:
  
  ```python
  from ultralyticsplus import YOLO, render_result
  
  # load model
  model = YOLO('ultralyticsplus/yolov8s')
  
  # 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()
  ```
  
  