readme:  
  ---
  tags:
  - ultralyticsplus
  - yolov8
  - ultralytics
  - yolo
  - vision
  - image-segmentation
  - pytorch
  library_name: ultralytics
  library_version: 8.0.6
  inference: false
  
  model-index:
  - name: fcakyon/test-model
    results:
    - task:
        type: image-segmentation
  
      metrics:
        - type: precision  # since mAP@0.5 is not available on hf.co/metrics
          value: 0.63311  # min: 0.0 - max: 1.0
          name: mAP@0.5(box)
        - type: precision  # since mAP@0.5 is not available on hf.co/metrics
          value: 0.60214  # min: 0.0 - max: 1.0
          name: mAP@0.5(mask)
  ---
  
  <div align="center">
    <img width="640" alt="fcakyon/test-model" src="https://huggingface.co/fcakyon/test-model/resolve/main/thumbnail.jpg">
  </div>
  
  ### Supported Labels
  
  ```
  ['Cracks-and-spalling', 'object']
  ```
  
  ### How to use
  
  - Install [ultralytics](https://github.com/ultralytics/ultralytics) and [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
  
  ```bash
  pip install -U ultralytics ultralyticsplus
  ```
  
  - Load model and perform prediction:
  
  ```python
  from ultralyticsplus import YOLO, render_model_output
  
  # load model
  model = YOLO('fcakyon/test-model')
  
  # 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]]
      print(result["segment"]) # [segmentation mask]
      render = render_model_output(model=model, image=image, model_output=result)
      render.show()
  ```
  
  