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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:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "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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