readme: ---
  license: gpl-3.0
  inference: false
  tags:
    - object-detection
    - computer-vision
    - vision
    - yolo
    - yolov5
  datasets:
    - detection-datasets/coco
  ---
  
  ### How to use
  
  - Install yolov5:
  
  ```bash
  pip install -U yolov5
  ```
  
  - Load model and perform prediction:
  
  ```python
  import yolov5
  
  # load model
  model = yolov5.load('fcakyon/yolov5s-v7.0')
    
  # 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)
  
  # inference with larger input size
  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 --img 640 --batch 16 --weights fcakyon/yolov5s-v7.0 --epochs 10 --device cuda:0
  ```