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  license: gpl-3.0
 
 
 
 
 
 
 
 
 
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  license: gpl-3.0
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+ inference: false
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+ tags:
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+ - object-detection
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+ - computer-vision
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+ - vision
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+ - yolo
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+ - yolov5
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+ datasets:
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+ - detection-datasets/coco
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  ---
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+
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+ ### How to use
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+
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+ - Install yolov5:
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+
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+ ```bash
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+ pip install -U yolov5
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+ ```
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+
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+ - Load model and perform prediction:
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+
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+ ```python
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+ import yolov5
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+
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+ # load model
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+ model = yolov5.load('fcakyon/yolov5n-v7.0')
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+
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+ # set model parameters
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+ model.conf = 0.25 # NMS confidence threshold
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+ model.iou = 0.45 # NMS IoU threshold
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+ model.agnostic = False # NMS class-agnostic
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+ model.multi_label = False # NMS multiple labels per box
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+ model.max_det = 1000 # maximum number of detections per image
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+
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+ # set image
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+ img = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
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+
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+ # perform inference
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+ results = model(img)
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+
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+ # inference with larger input size
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+ results = model(img, size=640)
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+
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+ # inference with test time augmentation
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+ results = model(img, augment=True)
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+
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+ # parse results
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+ predictions = results.pred[0]
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+ boxes = predictions[:, :4] # x1, y1, x2, y2
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+ scores = predictions[:, 4]
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+ categories = predictions[:, 5]
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+
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+ # show detection bounding boxes on image
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+ results.show()
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+
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+ # save results into "results/" folder
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+ results.save(save_dir='results/')
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+ ```