readme:  
  ---
  tags:
  - yolov5
  - yolo
  - vision
  - object-detection
  - pytorch
  library_name: yolov5
  library_version: 7.0.6
  inference: false
  
  datasets:
  - keremberke/blood-cell-object-detection
  
  model-index:
  - name: keremberke/yolov5m-blood-cell
    results:
    - task:
        type: object-detection
  
      dataset:
        type: keremberke/blood-cell-object-detection
        name: keremberke/blood-cell-object-detection
        split: validation
  
      metrics:
        - type: precision  # since mAP@0.5 is not available on hf.co/metrics
          value: 0.9052191412458869  # min: 0.0 - max: 1.0
          name: mAP@0.5
  ---
  
  <div align="center">
    <img width="640" alt="keremberke/yolov5m-blood-cell" src="https://huggingface.co/keremberke/yolov5m-blood-cell/resolve/main/sample_visuals.jpg">
  </div>
  
  ### How to use
  
  - Install [yolov5](https://github.com/fcakyon/yolov5-pip):
  
  ```bash
  pip install -U yolov5
  ```
  
  - Load model and perform prediction:
  
  ```python
  import yolov5
  
  # load model
  model = yolov5.load('keremberke/yolov5m-blood-cell')
    
  # set model parameters
  model.conf = 0.25  # NMS confidence threshold
  model.iou = 0.45  # NMS IoU threshold
  model.agnostic = False  # NMS class-agnostic
  model.multi_label = False  # NMS multiple labels per box
  model.max_det = 1000  # maximum number of detections per image
  
  # set image
  img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
  
  # perform inference
  results = model(img, size=640)
  
  # inference with test time augmentation
  results = model(img, augment=True)
  
  # parse results
  predictions = results.pred[0]
  boxes = predictions[:, :4] # x1, y1, x2, y2
  scores = predictions[:, 4]
  categories = predictions[:, 5]
  
  # show detection bounding boxes on image
  results.show()
  
  # save results into "results/" folder
  results.save(save_dir='results/')
  ```
  
  - Finetune the model on your custom dataset:
  
  ```bash
  yolov5 train --data data.yaml --img 640 --batch 16 --weights keremberke/yolov5m-blood-cell --epochs 10
  ```
  
  **More models available at: [awesome-yolov5-models](https://github.com/keremberke/awesome-yolov5-models)**