LPR.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "iwaUJGA9wZqH"
      },
      "outputs": [],
      "source": [
        "#Necessary Imports\n",
        "import tensorflow as tf\n",
        "from google.colab import drive\n",
        "import cv2\n",
        "import imghdr\n",
        "from matplotlib import pyplot as plt\n",
        "import os,io\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "bsl_7mu6mT0X"
      },
      "outputs": [],
      "source": [
        "#Confirming Gpus\n",
        "gpus=tf.config.experimental.list_physical_devices('GPU')\n",
        "for gpu in gpus:\n",
        "  tf.config.experimental.set_memory_growth(gpu,True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "WGtu0e8E9SNm"
      },
      "outputs": [],
      "source": [
        "#Preprocessing extensions for ease in faulty or dodgy data\n",
        "data_dir='/content/drive/MyDrive/Dataset'\n",
        "img_exts=['.jpeg','.jpg','.bmp','.png']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UleuCPgg-QM3",
        "outputId": "ff120bca-7fa1-4347-dd0b-3ea5ec068313"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Image not in extension list/content/drive/MyDrive/Dataset/google_images/018b52e6-e9a1-42c2-8ce7-0617e8c8e021___3e7fd381-0ae5-4421-8a70-279ee0ec1c61_sbtb02_auto1.JPG\n"
          ]
        }
      ],
      "source": [
        "#Cleaning corrupted data throughout the database\n",
        "for folders in os.listdir(data_dir):\n",
        "  for images in os.listdir(os.path.join(data_dir,folders)):\n",
        "    img_path=os.path.join(data_dir,folders,images)\n",
        "    try:\n",
        "      img=cv2.imread(img_path)\n",
        "      tip=imghdr.what(img_path)\n",
        "      file_name, file_extension = os.path.splitext(img_path)\n",
        "\n",
        "      if file_extension not in img_exts:\n",
        "        print('Image not in extension list{}'.format(img_path))\n",
        "        os.remove(img_path)\n",
        "\n",
        "\n",
        "    except Exception as e:\n",
        "        print((\"Dodgy image {}\".format(img_path)))\n",
        "        os.remove(img_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "6FuGEl8QkaHD",
        "outputId": "9d4b636b-2f36-41f6-ffb6-483db6ecffea"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Image preview for testing\n",
        "plt.imshow(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvoNKrzL-DJZ",
        "outputId": "aff00267-1bd3-424c-df43-22a596e2cdfb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.2.12-py3-none-any.whl (756 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/756.4 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/756.4 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.7/756.4 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m756.4/756.4 kB\u001b[0m \u001b[31m7.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.7.1)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.8.0.76)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.4.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.1)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.31.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.11.4)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.2.1+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.17.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.66.4)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Collecting thop>=0.1.1 (from ultralytics)\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.0.3)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.1)\n",
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            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, thop, ultralytics\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 thop-0.1.1.post2209072238 ultralytics-8.2.12\n"
          ]
        }
      ],
      "source": [
        "!pip install ultralytics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BT-2t-PT-XLQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9468f121-a6bd-4f83-8b99-e75055edbf75"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ultralytics YOLOv8.2.12 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.yaml, data=/content/drive/MyDrive/Indian Vehicles_YOLOv8_LPR/datasets/data.yaml, epochs=100, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, 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, freeze=None, multi_scale=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, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, 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, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 16.1MB/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overriding model.yaml nc=80 with nc=1\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    751507  ultralytics.nn.modules.head.Detect           [1, [64, 128, 256]]           \n",
            "YOLOv8n summary: 225 layers, 3011043 parameters, 3011027 gradients, 8.2 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
            "Freezing layer 'model.22.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n.pt to 'yolov8n.pt'...\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 48.0MB/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/Indian Vehicles_YOLOv8_LPR/datasets/train/labels.cache... 772 images, 12 backgrounds, 0 corrupt: 100%|██████████| 772/772 [00:00<?, ?it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Indian Vehicles_YOLOv8_LPR/datasets/valid/labels.cache... 220 images, 3 backgrounds, 0 corrupt: 100%|██████████| 220/220 [00:00<?, ?it/s]\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Plotting labels to runs/detect/train/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.002, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      1/100      2.45G      3.939      4.959      4.202          3        640: 100%|██████████| 49/49 [00:25<00:00,  1.91it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:14<00:00,  2.13s/it]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217   0.000258     0.0783   0.000153   4.82e-05\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "      2/100      2.28G      3.108       3.59      3.452          4        640: 100%|██████████| 49/49 [00:22<00:00,  2.15it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.39it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217    0.00171      0.194    0.00102   0.000278\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "metadata": {
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          },
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "      3/100      2.29G      2.839       2.83      3.072          8        640: 100%|██████████| 49/49 [00:21<00:00,  2.31it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.12it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.174      0.364      0.155     0.0425\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "      4/100      2.29G      2.545      2.382      2.853          4        640: 100%|██████████| 49/49 [00:24<00:00,  2.02it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.03it/s]"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "                   all        220        217        0.2      0.184      0.148     0.0583\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
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          "text": [
            "      5/100      2.29G      2.426      2.142      2.748          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.31it/s]"
          ]
        },
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.351      0.429      0.338     0.0953\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      6/100      2.29G      2.157      1.875      2.522         10        640: 100%|██████████| 49/49 [00:21<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.26it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.368      0.212       0.29      0.106\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "      7/100      2.29G      2.101      1.771       2.48          5        640: 100%|██████████| 49/49 [00:20<00:00,  2.35it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.42it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.679        0.7      0.744      0.351\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      8/100      2.29G      1.937      1.527      2.315          9        640: 100%|██████████| 49/49 [00:22<00:00,  2.19it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.39it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.518      0.613      0.544      0.238\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      9/100      2.29G      1.844      1.467      2.196          8        640: 100%|██████████| 49/49 [00:21<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.57it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.733       0.71      0.784      0.372\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     10/100      2.29G      1.798      1.405      2.181         10        640: 100%|██████████| 49/49 [00:21<00:00,  2.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.13it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217       0.86      0.811      0.885      0.463\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     11/100      2.29G      1.742      1.364       2.13          8        640: 100%|██████████| 49/49 [00:23<00:00,  2.05it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.39it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.862      0.839      0.893      0.444\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
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          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     12/100      2.29G      1.691      1.291      2.112          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.41it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217       0.92      0.798      0.906        0.5\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     13/100      2.29G      1.673      1.267      2.082          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.37it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.802      0.782      0.831      0.434\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
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          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     14/100      2.29G      1.668      1.298      2.032          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.27it/s]\n",
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          ]
        },
        {
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            "tags": null
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          "text": [
            "                   all        220        217      0.806      0.825      0.893      0.488\n"
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        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     15/100      2.29G      1.614      1.187      1.981          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.31it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.46it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217       0.89      0.848      0.916      0.495\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     16/100      2.29G      1.574      1.151      1.984          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.33it/s]\n",
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          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.779      0.793      0.821      0.391\n"
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        },
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          "output_type": "stream",
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            "\n"
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        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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          "output_type": "stream",
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            "     17/100      2.29G      1.544      1.128      1.968          6        640: 100%|██████████| 49/49 [00:22<00:00,  2.17it/s]\n",
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          ]
        },
        {
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            "tags": null
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          "text": [
            "                   all        220        217      0.915      0.849      0.915      0.519\n"
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        },
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          "output_type": "stream",
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            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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          "output_type": "stream",
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            "     18/100      2.29G      1.506      1.095      1.909          9        640: 100%|██████████| 49/49 [00:25<00:00,  1.95it/s]\n",
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        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.936      0.808      0.936      0.541\n"
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        },
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            "tags": null
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          "output_type": "stream",
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            "\n"
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        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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            "     19/100      2.29G      1.483      1.007       1.85          7        640: 100%|██████████| 49/49 [00:22<00:00,  2.22it/s]\n",
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          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.901      0.862      0.906      0.512\n"
          ]
        },
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          "output_type": "stream",
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            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
        {
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            "     20/100      2.29G      1.524      1.024      1.911          8        640: 100%|██████████| 49/49 [00:22<00:00,  2.15it/s]\n",
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        },
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          "text": [
            "                   all        220        217      0.922      0.848      0.923      0.511\n"
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        },
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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            "     21/100      2.29G      1.483       1.02      1.872          6        640: 100%|██████████| 49/49 [00:22<00:00,  2.19it/s]\n",
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        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.909      0.885      0.934      0.549\n"
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        },
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            "tags": null
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          "output_type": "stream",
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            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
        {
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            "tags": null
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            "     22/100      2.29G      1.468     0.9437      1.848          7        640: 100%|██████████| 49/49 [00:21<00:00,  2.23it/s]\n",
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          "text": [
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            "\n"
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        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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            "     23/100      2.29G      1.486     0.9643      1.866          9        640: 100%|██████████| 49/49 [00:22<00:00,  2.21it/s]\n",
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          "text": [
            "                   all        220        217      0.908      0.848      0.927      0.566\n"
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        },
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          "metadata": {
            "tags": null
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            "\n"
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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            "     24/100      2.29G      1.432     0.9732      1.817          3        640: 100%|██████████| 49/49 [00:24<00:00,  2.03it/s]\n",
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            "     25/100      2.29G       1.43     0.9294       1.83          7        640: 100%|██████████| 49/49 [00:22<00:00,  2.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.43it/s]"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "                   all        220        217      0.904      0.885      0.948      0.548\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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            "\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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            "     26/100      2.29G      1.406     0.9208      1.801          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.29it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.44it/s]"
          ]
        },
        {
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            "tags": null
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          "text": [
            "                   all        220        217      0.916      0.839      0.901      0.485\n"
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        },
        {
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            "tags": null
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            "\n"
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        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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            "     27/100      2.29G      1.384     0.9352      1.787         10        640: 100%|██████████| 49/49 [00:21<00:00,  2.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.25it/s]"
          ]
        },
        {
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            "tags": null
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          "text": [
            "                   all        220        217       0.89      0.899      0.944      0.595\n"
          ]
        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
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            "\n"
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        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "     28/100      2.29G      1.392      0.894      1.778          9        640: 100%|██████████| 49/49 [00:21<00:00,  2.27it/s]\n",
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          ]
        },
        {
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          "text": [
            "                   all        220        217      0.903      0.894      0.943      0.579\n"
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        },
        {
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            "tags": null
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          "output_type": "stream",
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            "\n"
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        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     29/100      2.29G      1.339     0.8425      1.747          7        640: 100%|██████████| 49/49 [00:20<00:00,  2.43it/s]\n",
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          ]
        },
        {
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          "text": [
            "                   all        220        217       0.93      0.866       0.95       0.57\n"
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        },
        {
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          "output_type": "stream",
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            "\n"
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        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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            "     30/100      2.29G      1.345     0.8553      1.741          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.29it/s]\n",
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          ]
        },
        {
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            "tags": null
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          "text": [
            "                   all        220        217      0.964      0.856      0.929      0.573\n"
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        },
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          "output_type": "stream",
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            "\n"
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "     31/100      2.29G      1.334     0.8802      1.733         10        640: 100%|██████████| 49/49 [00:22<00:00,  2.15it/s]\n",
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          ]
        },
        {
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            "tags": null
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          "text": [
            "                   all        220        217      0.928      0.837      0.927      0.546\n"
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        },
        {
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          "output_type": "stream",
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            "\n"
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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            "     32/100      2.29G      1.352     0.8973      1.757          8        640: 100%|██████████| 49/49 [00:21<00:00,  2.28it/s]\n",
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        },
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          "text": [
            "                   all        220        217      0.933      0.848      0.913      0.512\n"
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        },
        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n"
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        {
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            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
        {
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          "output_type": "stream",
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            "     33/100      2.29G      1.324     0.8621      1.724          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.31it/s]\n",
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          ]
        },
        {
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            "                   all        220        217      0.913       0.87      0.942       0.58\n"
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        },
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          "output_type": "stream",
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            "\n"
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          "output_type": "stream",
          "text": [
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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            "     34/100      2.28G      1.307     0.8474      1.717          3        640: 100%|██████████| 49/49 [00:21<00:00,  2.28it/s]\n",
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        },
        {
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            "                   all        220        217      0.955      0.876      0.952      0.605\n"
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          "output_type": "stream",
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        {
          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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          "output_type": "stream",
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            "     35/100      2.29G      1.275     0.8204      1.686          8        640: 100%|██████████| 49/49 [00:22<00:00,  2.20it/s]\n",
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        },
        {
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          "text": [
            "                   all        220        217       0.89      0.897      0.943       0.54\n"
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          "output_type": "stream",
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          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        },
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            "     36/100      2.29G      1.291     0.8253      1.718          9        640: 100%|██████████| 49/49 [00:21<00:00,  2.28it/s]\n",
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        },
        {
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          "text": [
            "                   all        220        217      0.884       0.91      0.952      0.599\n"
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          "output_type": "stream",
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            "tags": null
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          "output_type": "stream",
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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            "     37/100      2.29G      1.244     0.8115      1.666          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.33it/s]\n",
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          "output_type": "stream",
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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          "text": [
            "                   all        220        217      0.959      0.899       0.95      0.586\n"
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          "output_type": "stream",
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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          "text": [
            "                   all        220        217      0.958      0.894      0.952      0.603\n"
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          "output_type": "stream",
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            "\n"
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          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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        {
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            "                   all        220        217      0.929      0.901      0.944      0.621\n"
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            "\n"
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            "tags": null
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          "output_type": "stream",
          "text": [
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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            "                   all        220        217      0.942      0.903      0.962       0.61\n"
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          "metadata": {
            "tags": null
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          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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          "text": [
            "                   all        220        217      0.929      0.899       0.94       0.55\n"
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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            "                   all        220        217      0.951      0.898      0.966      0.634\n"
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            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
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          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217       0.96      0.885      0.957      0.623\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     45/100      2.29G       1.23     0.7562      1.636          6        640: 100%|██████████| 49/49 [00:22<00:00,  2.21it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.32it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.962      0.876      0.961      0.626\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     46/100      2.29G      1.227     0.7272      1.627          6        640: 100%|██████████| 49/49 [00:20<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.43it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.946      0.912      0.956       0.59\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
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        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     47/100      2.29G      1.253     0.7815      1.666          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.44it/s]"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all        220        217      0.959      0.912       0.97      0.644\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     48/100      2.29G      1.231     0.7581      1.633          8        640: 100%|██████████| 49/49 [00:21<00:00,  2.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.61it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.943      0.908      0.959      0.617\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     49/100      2.29G      1.236     0.7468      1.628          5        640: 100%|██████████| 49/49 [00:20<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.67it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217       0.96      0.891      0.967      0.611\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     50/100      2.29G      1.232     0.7389      1.638          7        640: 100%|██████████| 49/49 [00:21<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.18it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.938      0.894      0.959      0.623\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     51/100      2.29G      1.173     0.7247      1.587          8        640: 100%|██████████| 49/49 [00:20<00:00,  2.35it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.62it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.916      0.931      0.964      0.632\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     52/100      2.29G      1.199     0.6973      1.601         10        640: 100%|██████████| 49/49 [00:20<00:00,  2.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.37it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.939      0.912      0.953      0.619\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     53/100      2.29G      1.246     0.7392      1.645          4        640: 100%|██████████| 49/49 [00:23<00:00,  2.10it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.02it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.948      0.931      0.967      0.608\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     54/100      2.29G      1.241     0.7461      1.616          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217       0.94      0.938      0.961      0.637\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     55/100      2.29G      1.163     0.7088      1.588          8        640: 100%|██████████| 49/49 [00:20<00:00,  2.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.966      0.917      0.963      0.622\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     56/100      2.29G      1.141     0.7062      1.577          3        640: 100%|██████████| 49/49 [00:21<00:00,  2.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.26it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.961      0.901      0.952      0.621\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     57/100      2.29G      1.156     0.6973      1.587          3        640: 100%|██████████| 49/49 [00:20<00:00,  2.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.59it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.944      0.912      0.958      0.629\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     58/100      2.28G      1.202      0.713      1.628          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217       0.96      0.885      0.957      0.632\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     59/100      2.29G      1.131     0.7148      1.552          1        640: 100%|██████████| 49/49 [00:21<00:00,  2.31it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.72it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.949      0.937      0.968      0.605\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     60/100      2.29G      1.195     0.6977      1.614          8        640: 100%|██████████| 49/49 [00:22<00:00,  2.16it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  1.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.957      0.924      0.968       0.64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     61/100      2.29G      1.181     0.6756      1.594         10        640: 100%|██████████| 49/49 [00:22<00:00,  2.21it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.14it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.936      0.935      0.973      0.642\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     62/100      2.28G      1.142      0.684      1.564          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.24it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.945      0.922      0.962      0.639\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     63/100      2.29G      1.145     0.6741       1.56          9        640: 100%|██████████| 49/49 [00:21<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.56it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.957      0.915       0.96       0.62\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     64/100      2.29G      1.155     0.6631      1.601         11        640: 100%|██████████| 49/49 [00:21<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.73it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.968      0.912      0.976      0.657\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     65/100      2.29G      1.144     0.6563      1.607          7        640: 100%|██████████| 49/49 [00:20<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.27it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.936      0.949      0.974      0.632\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     66/100      2.29G      1.149     0.6623       1.55          6        640: 100%|██████████| 49/49 [00:20<00:00,  2.37it/s]\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.966      0.926      0.973      0.644\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     67/100      2.29G      1.159     0.6755      1.564          9        640: 100%|██████████| 49/49 [00:25<00:00,  1.93it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.79it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.915      0.945      0.969      0.636\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     68/100      2.29G      1.139      0.665      1.568          5        640: 100%|██████████| 49/49 [00:20<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.05it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.953      0.936      0.964      0.635\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     69/100      2.29G      1.102     0.6414      1.543          8        640: 100%|██████████| 49/49 [00:22<00:00,  2.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.63it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.949      0.935      0.973       0.64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     70/100      2.29G      1.117     0.6587      1.558          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.83it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.962      0.936      0.961      0.645\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     71/100      2.29G      1.123      0.638      1.544          8        640: 100%|██████████| 49/49 [00:20<00:00,  2.33it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.944       0.94      0.969      0.659\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     72/100      2.29G      1.051     0.6223      1.504          4        640: 100%|██████████| 49/49 [00:22<00:00,  2.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.953      0.931      0.967      0.652\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     73/100      2.29G      1.109     0.6488      1.515          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.945      0.949      0.971      0.637\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     74/100      2.28G      1.094     0.6149      1.559          3        640: 100%|██████████| 49/49 [00:23<00:00,  2.08it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.68it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.953      0.932       0.97      0.664\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     75/100      2.29G      1.106      0.633      1.544          8        640: 100%|██████████| 49/49 [00:22<00:00,  2.16it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.07it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.952      0.935      0.958      0.648\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     76/100      2.29G      1.077     0.6015      1.541          7        640: 100%|██████████| 49/49 [00:20<00:00,  2.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.35it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.952      0.931      0.963      0.645\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     77/100      2.29G      1.101      0.641      1.535          3        640: 100%|██████████| 49/49 [00:22<00:00,  2.19it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.39it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217       0.97      0.931      0.965      0.663\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     78/100      2.29G      1.078     0.6232      1.517         10        640: 100%|██████████| 49/49 [00:21<00:00,  2.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.933      0.945      0.971      0.667\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     79/100      2.29G      1.084     0.6156      1.507          5        640: 100%|██████████| 49/49 [00:21<00:00,  2.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.28it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.949      0.945      0.967      0.651\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     80/100      2.29G      1.073     0.6241      1.515          3        640: 100%|██████████| 49/49 [00:22<00:00,  2.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.32it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.951       0.94      0.968      0.651\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     81/100      2.29G       1.04     0.5968      1.517          3        640: 100%|██████████| 49/49 [00:24<00:00,  1.98it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.953      0.945      0.971      0.668\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     82/100      2.29G      1.027      0.595      1.468          3        640: 100%|██████████| 49/49 [00:20<00:00,  2.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.34it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.962       0.94       0.97      0.661\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     83/100      2.29G      1.083     0.6034      1.533          8        640: 100%|██████████| 49/49 [00:21<00:00,  2.24it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.42it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.964      0.931      0.971       0.66\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     84/100      2.29G      1.037     0.5776      1.497         10        640: 100%|██████████| 49/49 [00:20<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.949      0.951      0.968      0.679\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     85/100      2.29G      1.017     0.5799      1.452          8        640: 100%|██████████| 49/49 [00:22<00:00,  2.18it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.29it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.962      0.949      0.969       0.67\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     86/100      2.29G      1.018     0.5728       1.46         11        640: 100%|██████████| 49/49 [00:20<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.47it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.962      0.935      0.972      0.676\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     87/100      2.29G      1.004     0.5686      1.456          7        640: 100%|██████████| 49/49 [00:21<00:00,  2.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.966      0.935      0.969      0.668\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     88/100      2.29G       1.03     0.5925      1.479          5        640: 100%|██████████| 49/49 [00:23<00:00,  2.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.35it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.954      0.948      0.968      0.672\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     89/100      2.29G      1.029     0.5828      1.477          7        640: 100%|██████████| 49/49 [00:21<00:00,  2.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.46it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.965      0.945       0.97       0.67\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     90/100      2.29G      1.034     0.5771      1.476          6        640: 100%|██████████| 49/49 [00:21<00:00,  2.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.58it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.967      0.931      0.972      0.671\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Closing dataloader mosaic\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     91/100      2.42G     0.9525     0.5239      1.475          4        640: 100%|██████████| 49/49 [00:23<00:00,  2.06it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.59it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217       0.96      0.931      0.964      0.663\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     92/100      2.29G     0.9315     0.4982      1.463          4        640: 100%|██████████| 49/49 [00:21<00:00,  2.29it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.47it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.967      0.951      0.972      0.679\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     93/100      2.29G     0.9109     0.4988       1.46          4        640: 100%|██████████| 49/49 [00:21<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.29it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.954      0.955      0.975       0.69\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     94/100      2.28G     0.8994     0.4848      1.429          4        640: 100%|██████████| 49/49 [00:23<00:00,  2.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.31it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.966      0.945      0.972      0.684\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     95/100      2.29G     0.9094     0.4918      1.478          4        640: 100%|██████████| 49/49 [00:20<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.75it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.963      0.947      0.973      0.692\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     96/100      2.29G     0.8856     0.4776      1.419          4        640: 100%|██████████| 49/49 [00:21<00:00,  2.24it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:04<00:00,  1.69it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.946      0.963      0.973      0.698\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     97/100      2.29G     0.8704     0.4634      1.419          4        640: 100%|██████████| 49/49 [00:21<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.71it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.954      0.954      0.973      0.697\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     98/100      2.28G     0.8806      0.467      1.433          4        640: 100%|██████████| 49/49 [00:22<00:00,  2.21it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:03<00:00,  2.14it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.953      0.954      0.973      0.704\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "     99/100      2.29G     0.8801     0.4805      1.446          4        640: 100%|██████████| 49/49 [00:21<00:00,  2.24it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:05<00:00,  1.25it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.954      0.956       0.97      0.695\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "    100/100      2.29G     0.9009      0.462      1.423          4        640: 100%|██████████| 49/49 [00:21<00:00,  2.33it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:02<00:00,  2.50it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.958      0.951      0.972      0.692\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "100 epochs completed in 0.767 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 6.3MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 6.3MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics YOLOv8.2.12 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8n summary (fused): 168 layers, 3005843 parameters, 0 gradients, 8.1 GFLOPs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.05s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        220        217      0.952      0.954      0.973      0.704\n",
            "Speed: 0.4ms preprocess, 3.6ms inference, 0.0ms loss, 8.8ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from ultralytics import YOLO\n",
        "\n",
        "#load a model\n",
        "model=YOLO(\"yolov8n.yaml\")\n",
        "#Use the model\n",
        "results=model.train(data=os.path.join(data_dir,\"data.yaml\"), epochs=100)  # Model Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3b521JqFA8IS"
      },
      "outputs": [],
      "source": [
        "#Download the stats\n",
        "from google.colab import files\n",
        "files.download('runs')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!scp -r/content/runs '/content/drive/MyDrive/Indian Vehicles_YOLOv8_LPR/datasets'\n"
      ],
      "metadata": {
        "id": "nTT2lXpOqklL",
        "outputId": "f595c4d5-929d-4133-c7b6-0117417bec12",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "scp: unknown option -- /\r\n",
            "usage: scp [-346ABCOpqRrsTv] [-c cipher] [-D sftp_server_path] [-F ssh_config]\n",
            "           [-i identity_file] [-J destination] [-l limit]\n",
            "           [-o ssh_option] [-P port] [-S program] source ... target\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}