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{
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"## How tu use\n",
"\n",
"- Install [yolov5](https://github.com/fcakyon/yolov5-pip):\n",
"\n",
"```bash\n",
"pip install -U yolov5\n",
"```\n",
"\n",
"- Load model and perform prediction:"
]
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{
"cell_type": "code",
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"source": [
"import yolov5\n",
"\n",
"# load model\n",
"model = yolov5.load('best.pt')\n",
" \n",
"# set model parameters\n",
"model.conf = 0.25 # NMS confidence threshold\n",
"model.iou = 0.45 # NMS IoU threshold\n",
"model.agnostic = False # NMS class-agnostic\n",
"model.multi_label = False # NMS multiple labels per box\n",
"model.max_det = 1000 # maximum number of detections per image\n",
"\n",
"# set image\n",
"img = 'https://dl.ndl.go.jp/api/iiif/2534020/T0000001/full/full/0/default.jpg'\n",
"\n",
"# perform inference\n",
"results = model(img, size=640)\n",
"\n",
"# inference with test time augmentation\n",
"results = model(img, augment=True)\n",
"\n",
"# parse results\n",
"predictions = results.pred[0]\n",
"boxes = predictions[:, :4] # x1, y1, x2, y2\n",
"scores = predictions[:, 4]\n",
"categories = predictions[:, 5]\n",
"\n",
"# show detection bounding boxes on image\n",
"results.show()\n",
"\n",
"# save results into \"results/\" folder\n",
"results.save(save_dir='results/')\n"
]
}
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