{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -r requirement.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b1ac199-738d-483e-93cb-6624fd9b9de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ultralytics import YOLO\n",
    "\n",
    "# Load a model\n",
    "model = YOLO(\"yolov8n.yaml\")  # build a new model from scratch\n",
    "model = YOLO(\"yolov8n.pt\")  # load a pretrained model (recommended for training)\n",
    "\n",
    "# Use the model\n",
    "model.train(data=\"coco128.yaml\", epochs=3)  # train the model\n",
    "metrics = model.val()  # evaluate model performance on the validation set\n",
    "results = model(\"https://ultralytics.com/images/bus.jpg\")  # predict on an image\n",
    "path = model.export(format=\"onnx\")  # export the model to ONNX format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb73b4b6-d444-4059-b64a-03479b4ab05c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "                   from  n    params  module                                       arguments                     \n",
    "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
    "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
    "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
    "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
    "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
    "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
    "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
    "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
    "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
    "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
    " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
    " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
    " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
    " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
    " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
    " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
    " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
    " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
    " 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]          \n",
    "YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients\n",
    "\n",
    "Ultralytics YOLOv8.0.145 🚀 Python-3.7.10 torch-1.13.1+cu117 CPU (Hygon C86 7185 32-core Processor)\n",
    "WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.\n",
    "engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=coco128.yaml, epochs=3, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train7\n",
    "\n",
    "                   from  n    params  module                                       arguments                     \n",
    "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
    "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
    "  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             \n",
    "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
    "  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             \n",
    "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
    "  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
    "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
    "  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           \n",
    "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
    " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
    " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
    " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
    " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  \n",
    " 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
    " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 \n",
    " 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
    " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
    " 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
    " 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.head.Detect           [80, [64, 128, 256]]          \n",
    "Model summary: 225 layers, 3157200 parameters, 3157184 gradients\n",
    "\n",
    "Transferred 355/355 items from pretrained weights\n",
    "TensorBoard: Start with 'tensorboard --logdir runs/detect/train7', view at http://localhost:6006/\n",
    "train: Scanning /public/home/jsyadmin/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:00<?, ?it/s]\n",
    "val: Scanning /public/home/jsyadmin/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:00<?, ?it/s]\n",
    "Plotting labels to runs/detect/train7/labels.jpg... \n",
    "optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
    "Image sizes 640 train, 640 val\n",
    "Using 0 dataloader workers\n",
    "Logging results to runs/detect/train7\n",
    "Starting training for 3 epochs...\n",
    "\n",
    "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
    "        1/3         0G      1.096      1.365      1.202        201        640: 100%|██████████| 8/8 [00:50<00:00,  6.30s/it]\n",
    "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:15<00:00,  3.85s/it]\n",
    "                   all        128        929      0.658      0.532      0.615      0.457\n",
    "\n",
    "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
    "        2/3         0G      1.216      1.443      1.268        136        640: 100%|██████████| 8/8 [00:46<00:00,  5.85s/it]\n",
    "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:15<00:00,  3.80s/it]\n",
    "                   all        128        929      0.672      0.542      0.628      0.466\n",
    "\n",
    "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
    "        3/3         0G      1.193      1.342      1.243        206        640: 100%|██████████| 8/8 [00:47<00:00,  5.92s/it]\n",
    "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:15<00:00,  3.94s/it]\n",
    "                   all        128        929       0.69      0.532      0.631      0.468\n",
    "\n",
    "3 epochs completed in 0.057 hours.\n",
    "Optimizer stripped from runs/detect/train7/weights/last.pt, 6.5MB\n",
    "Optimizer stripped from runs/detect/train7/weights/best.pt, 6.5MB\n",
    "\n",
    "Validating runs/detect/train7/weights/best.pt...\n",
    "Ultralytics YOLOv8.0.145 🚀 Python-3.7.10 torch-1.13.1+cu117 CPU (Hygon C86 7185 32-core Processor)\n",
    "Model summary (fused): 168 layers, 3151904 parameters, 0 gradients\n",
    "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:14<00:00,  3.53s/it]\n",
    "                   all        128        929      0.691      0.531      0.631      0.469\n",
    "                person        128        254      0.818      0.655      0.764      0.538\n",
    "               bicycle        128          6      0.663      0.329      0.331       0.26\n",
    "                   car        128         46      0.867      0.217      0.279      0.174\n",
    "            motorcycle        128          5      0.678      0.847      0.938      0.743\n",
    "              airplane        128          6      0.817      0.752      0.927      0.687\n",
    "                   bus        128          7      0.753      0.714      0.728      0.671\n",
    "                 train        128          3      0.565      0.667       0.83      0.681\n",
    "                 truck        128         12          1      0.327      0.522      0.328\n",
    "                  boat        128          6      0.299      0.167       0.36      0.233\n",
    "         traffic light        128         14      0.738      0.204      0.201      0.138\n",
    "             stop sign        128          2          1      0.971      0.995      0.707\n",
    "                 bench        128          9      0.815      0.494      0.633      0.382\n",
    "                  bird        128         16      0.911      0.625      0.894      0.556\n",
    "                   cat        128          4      0.876          1      0.995      0.791\n",
    "                   dog        128          9      0.658      0.889      0.871      0.648\n",
    "                 horse        128          2      0.574          1      0.995      0.518\n",
    "              elephant        128         17      0.848      0.824      0.918      0.674\n",
    "                  bear        128          1      0.624          1      0.995      0.895\n",
    "                 zebra        128          4      0.865          1      0.995      0.965\n",
    "               giraffe        128          9      0.898       0.98      0.973      0.713\n",
    "              backpack        128          6      0.648      0.333      0.379       0.22\n",
    "              umbrella        128         18      0.814        0.5      0.668      0.465\n",
    "               handbag        128         19          1          0      0.207      0.126\n",
    "                   tie        128          7       0.83        0.7      0.677      0.495\n",
    "              suitcase        128          4       0.61      0.787      0.828      0.592\n",
    "               frisbee        128          5       0.63        0.8       0.76      0.664\n",
    "                  skis        128          1      0.746          1      0.995      0.497\n",
    "             snowboard        128          7       0.75      0.714      0.755      0.492\n",
    "           sports ball        128          6      0.689      0.378      0.502      0.274\n",
    "                  kite        128         10      0.805      0.415      0.588      0.203\n",
    "          baseball bat        128          4       0.45       0.25      0.378      0.187\n",
    "        baseball glove        128          7      0.679      0.429      0.429      0.294\n",
    "            skateboard        128          5      0.811        0.6      0.599      0.446\n",
    "         tennis racket        128          7      0.695      0.334      0.502      0.365\n",
    "                bottle        128         18      0.508      0.344      0.366      0.219\n",
    "            wine glass        128         16      0.795      0.487       0.61      0.363\n",
    "                   cup        128         36      0.651       0.26      0.402      0.289\n",
    "                  fork        128          6      0.609      0.167      0.294      0.205\n",
    "                 knife        128         16      0.745      0.549      0.628      0.362\n",
    "                 spoon        128         22      0.511      0.191      0.349      0.182\n",
    "                  bowl        128         28      0.693      0.679      0.641      0.528\n",
    "                banana        128          1          0          0      0.111     0.0369\n",
    "              sandwich        128          2      0.323        0.5      0.695      0.695\n",
    "                orange        128          4          1      0.334      0.995      0.666\n",
    "              broccoli        128         11      0.396      0.182      0.254      0.213\n",
    "                carrot        128         24      0.818      0.458      0.673      0.434\n",
    "               hot dog        128          2      0.634          1      0.828      0.795\n",
    "                 pizza        128          5      0.791          1      0.995      0.843\n",
    "                 donut        128         14       0.65          1      0.926       0.84\n",
    "                  cake        128          4      0.725          1      0.995       0.88\n",
    "                 chair        128         35      0.565      0.514      0.462       0.26\n",
    "                 couch        128          6      0.423      0.333      0.614      0.469\n",
    "          potted plant        128         14      0.818      0.643      0.722      0.498\n",
    "                   bed        128          3      0.793      0.667      0.775      0.676\n",
    "          dining table        128         13      0.512      0.615      0.514       0.41\n",
    "                toilet        128          2      0.644        0.5      0.745      0.721\n",
    "                    tv        128          2      0.543       0.63      0.828      0.762\n",
    "                laptop        128          3          1          0      0.665      0.522\n",
    "                 mouse        128          2          1          0     0.0443    0.00443\n",
    "                remote        128          8      0.874        0.5      0.578      0.502\n",
    "            cell phone        128          8          0          0     0.0582     0.0384\n",
    "             microwave        128          3        0.6      0.667       0.83      0.699\n",
    "                  oven        128          5      0.456        0.4      0.343      0.272\n",
    "                  sink        128          6      0.372      0.167      0.203      0.125\n",
    "          refrigerator        128          5      0.667        0.4       0.65      0.522\n",
    "                  book        128         29      0.619      0.113      0.355       0.18\n",
    "                 clock        128          9      0.778      0.781      0.891      0.721\n",
    "                  vase        128          2      0.411          1      0.828      0.795\n",
    "              scissors        128          1          1          0      0.249     0.0746\n",
    "            teddy bear        128         21      0.939      0.333      0.636      0.431\n",
    "            toothbrush        128          5      0.747        0.4      0.672      0.424\n",
    "Speed: 1.0ms preprocess, 91.7ms inference, 0.0ms loss, 2.2ms postprocess per image\n",
    "Results saved to runs/detect/train7\n",
    "Ultralytics YOLOv8.0.145 🚀 Python-3.7.10 torch-1.13.1+cu117 CPU (Hygon C86 7185 32-core Processor)\n",
    "Model summary (fused): 168 layers, 3151904 parameters, 0 gradients\n",
    "val: Scanning /public/home/jsyadmin/datasets/coco128/labels/train2017.cache... 126 images, 2 backgrounds, 0 corrupt: 100%|██████████| 128/128 [00:00<?, ?it/s]\n",
    "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:12<00:00,  1.62s/it]\n",
    "                   all        128        929      0.677      0.547      0.626      0.463\n",
    "                person        128        254      0.805      0.665      0.766      0.539\n",
    "               bicycle        128          6      0.499      0.333      0.328      0.258\n",
    "                   car        128         46      0.816      0.217      0.279      0.174\n",
    "            motorcycle        128          5      0.684      0.875      0.938      0.743\n",
    "              airplane        128          6       0.82      0.767      0.927      0.687\n",
    "                   bus        128          7       0.74      0.714      0.728      0.671\n",
    "                 train        128          3      0.555      0.667       0.83      0.681\n",
    "                 truck        128         12          1      0.371      0.496      0.286\n",
    "                  boat        128          6      0.238      0.167      0.327      0.205\n",
    "         traffic light        128         14      0.738      0.204      0.201      0.138\n",
    "             stop sign        128          2          1      0.989      0.995      0.708\n",
    "                 bench        128          9      0.825      0.526      0.632      0.382\n",
    "                  bird        128         16       0.85      0.708      0.884      0.549\n",
    "                   cat        128          4      0.867          1      0.995      0.791\n",
    "                   dog        128          9      0.649      0.889      0.871      0.648\n",
    "                 horse        128          2      0.513          1      0.995      0.518\n",
    "              elephant        128         17      0.825      0.829      0.918      0.674\n",
    "                  bear        128          1      0.609          1      0.995      0.895\n",
    "                 zebra        128          4       0.86          1      0.995      0.965\n",
    "               giraffe        128          9      0.807      0.935      0.951      0.753\n",
    "              backpack        128          6      0.629      0.333      0.379      0.231\n",
    "              umbrella        128         18      0.703        0.5      0.663      0.462\n",
    "               handbag        128         19          1     0.0683      0.206      0.126\n",
    "                   tie        128          7      0.827      0.714      0.677      0.495\n",
    "              suitcase        128          4      0.662          1      0.828      0.592\n",
    "               frisbee        128          5      0.604        0.8      0.759      0.664\n",
    "                  skis        128          1      0.627          1      0.995      0.497\n",
    "             snowboard        128          7      0.727      0.714      0.755      0.491\n",
    "           sports ball        128          6      0.698      0.396      0.502      0.274\n",
    "                  kite        128         10      0.824      0.472      0.589      0.204\n",
    "          baseball bat        128          4      0.526       0.25      0.353      0.199\n",
    "        baseball glove        128          7      0.641      0.429      0.429      0.316\n",
    "            skateboard        128          5      0.863        0.6      0.599       0.44\n",
    "         tennis racket        128          7      0.708      0.357      0.502      0.337\n",
    "                bottle        128         18      0.524      0.369      0.368      0.217\n",
    "            wine glass        128         16      0.776      0.433      0.569      0.354\n",
    "                   cup        128         36      0.671      0.278      0.419      0.302\n",
    "                  fork        128          6      0.584      0.167      0.236      0.183\n",
    "                 knife        128         16       0.65      0.562      0.607      0.353\n",
    "                 spoon        128         22      0.595      0.202      0.351      0.197\n",
    "                  bowl        128         28      0.681      0.687      0.667      0.517\n",
    "                banana        128          1          0          0     0.0829     0.0344\n",
    "              sandwich        128          2      0.323        0.5      0.308      0.308\n",
    "                orange        128          4          1      0.371      0.995      0.666\n",
    "              broccoli        128         11      0.358      0.182      0.263      0.214\n",
    "                carrot        128         24      0.736       0.58      0.669      0.428\n",
    "               hot dog        128          2      0.658      0.974      0.828      0.828\n",
    "                 pizza        128          5      0.857          1      0.995      0.843\n",
    "                 donut        128         14      0.647          1      0.926      0.841\n",
    "                  cake        128          4      0.612          1      0.995       0.88\n",
    "                 chair        128         35       0.54      0.514      0.451      0.249\n",
    "                 couch        128          6      0.524        0.5      0.715      0.553\n",
    "          potted plant        128         14      0.784      0.643      0.722      0.498\n",
    "                   bed        128          3      0.764      0.667      0.863      0.655\n",
    "          dining table        128         13      0.465      0.538      0.486      0.387\n",
    "                toilet        128          2      0.629        0.5      0.745      0.721\n",
    "                    tv        128          2      0.563       0.69      0.828      0.762\n",
    "                laptop        128          3          1          0        0.6       0.48\n",
    "                 mouse        128          2          1          0     0.0683    0.00683\n",
    "                remote        128          8      0.858        0.5      0.613      0.523\n",
    "            cell phone        128          8          0          0     0.0576     0.0383\n",
    "             microwave        128          3      0.566      0.667       0.83      0.683\n",
    "                  oven        128          5      0.459        0.4      0.343      0.272\n",
    "                  sink        128          6      0.383      0.167      0.166      0.104\n",
    "          refrigerator        128          5      0.638        0.4      0.654      0.506\n",
    "                  book        128         29      0.638      0.123      0.376      0.201\n",
    "                 clock        128          9      0.782      0.798      0.892      0.722\n",
    "                  vase        128          2      0.367          1      0.828      0.795\n",
    "              scissors        128          1          1          0      0.249     0.0746\n",
    "            teddy bear        128         21       0.92      0.333      0.632      0.428\n",
    "            toothbrush        128          5      0.745        0.6      0.743      0.478\n",
    "Speed: 0.8ms preprocess, 86.0ms inference, 0.0ms loss, 2.4ms postprocess per image\n",
    "Results saved to runs/detect/val3\n",
    "\n",
    "Found https://ultralytics.com/images/bus.jpg locally at bus.jpg\n",
    "image 1/1 /public/home/jsyadmin/yolotest/bus.jpg: 640x480 4 persons, 1 bus, 399.2ms\n",
    "Speed: 67.8ms preprocess, 399.2ms inference, 44.2ms postprocess per image at shape (1, 3, 640, 480)\n",
    "Ultralytics YOLOv8.0.145 🚀 Python-3.7.10 torch-1.13.1+cu117 CPU (Hygon C86 7185 32-core Processor)\n",
    "\n",
    "PyTorch: starting from 'runs/detect/train7/weights/best.pt' with input shape (1, 3, 640, 640) BCHW and output shape(s) (1, 84, 8400) (6.2 MB)\n",
    "\n",
    "ONNX: starting export with onnx 1.14.1 opset 16...\n",
    "ONNX: export success ✅ 2.6s, saved as 'runs/detect/train7/weights/best.onnx' (12.2 MB)\n",
    "\n",
    "Export complete (3.0s)\n",
    "Results saved to /public/home/jsyadmin/yolotest/runs/detect/train7/weights\n",
    "Predict:         yolo predict task=detect model=runs/detect/train7/weights/best.onnx imgsz=640 \n",
    "Validate:        yolo val task=detect model=runs/detect/train7/weights/best.onnx imgsz=640 data=None \n",
    "Visualize:       https://netron.app"
   ]
  }
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