Yolov9_for_parking - ipynb.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUghga1t1X3I",
        "outputId": "7931e491-1906-4607-f100-5339ca22d247"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.26)\n",
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          ]
        }
      ],
      "source": [
        "!pip install roboflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yf80Kv2s13zJ",
        "outputId": "04ce41de-797d-4312-9643-2e04e00118b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.1.46-py3-none-any.whl (750 kB)\n",
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            "Collecting thop>=0.1.1 (from ultralytics)\n",
            "  Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n",
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            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch>=1.8.0->ultralytics)\n",
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            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.19.3 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2.2.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.8.0->ultralytics)\n",
            "  Using cached nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from cycler>=0.10->matplotlib>=3.3.0->ultralytics) (1.16.0)\n",
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            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "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-8.1.46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#custom dataset was used and added to roboflow and the api was used in this code"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bJ8_KUVg2HTP",
        "outputId": "5d1633fd-9bac-431e-b678-e0242030af8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading Dataset Version Zip in SMART_PARKING-2 to yolov9:: 100%|██████████| 124934/124934 [00:05<00:00, 24227.07it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to SMART_PARKING-2 in yolov9:: 100%|██████████| 2914/2914 [00:00<00:00, 4401.37it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "# Load the YOLOv9c model\n",
        "model = YOLO(\"yolov9c.yaml\")\n",
        "\n",
        "# Set the dataset path and other training parameters\n",
        "model.train(data=\"/content/SMART_PARKING-2/data.yaml\", epochs=40, imgsz=640)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTaKi7Ai3ykE",
        "outputId": "15571639-bdd6-4e9b-e542-2b18bac82caf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.1.46 🚀 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=yolov9c.yaml, data=/content/SMART_PARKING-2/data.yaml, epochs=40, 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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 43.2MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overriding model.yaml nc=80 with nc=2\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n",
            "  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  2                  -1  1    212864  ultralytics.nn.modules.block.RepNCSPELAN4    [128, 256, 128, 64, 1]        \n",
            "  3                  -1  1    164352  ultralytics.nn.modules.block.ADown           [256, 256]                    \n",
            "  4                  -1  1    847616  ultralytics.nn.modules.block.RepNCSPELAN4    [256, 512, 256, 128, 1]       \n",
            "  5                  -1  1    656384  ultralytics.nn.modules.block.ADown           [512, 512]                    \n",
            "  6                  -1  1   2857472  ultralytics.nn.modules.block.RepNCSPELAN4    [512, 512, 512, 256, 1]       \n",
            "  7                  -1  1    656384  ultralytics.nn.modules.block.ADown           [512, 512]                    \n",
            "  8                  -1  1   2857472  ultralytics.nn.modules.block.RepNCSPELAN4    [512, 512, 512, 256, 1]       \n",
            "  9                  -1  1    656896  ultralytics.nn.modules.block.SPPELAN         [512, 512, 256]               \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   3119616  ultralytics.nn.modules.block.RepNCSPELAN4    [1024, 512, 512, 256, 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    912640  ultralytics.nn.modules.block.RepNCSPELAN4    [1024, 256, 256, 128, 1]      \n",
            " 16                  -1  1    164352  ultralytics.nn.modules.block.ADown           [256, 256]                    \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  1   2988544  ultralytics.nn.modules.block.RepNCSPELAN4    [768, 512, 512, 256, 1]       \n",
            " 19                  -1  1    656384  ultralytics.nn.modules.block.ADown           [512, 512]                    \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  1   3119616  ultralytics.nn.modules.block.RepNCSPELAN4    [1024, 512, 512, 256, 1]      \n",
            " 22        [15, 18, 21]  1   5584342  ultralytics.nn.modules.head.Detect           [2, [256, 512, 512]]          \n",
            "YOLOv9c summary: 618 layers, 25530774 parameters, 25530758 gradients, 103.7 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/v8.1.0/yolov8n.pt to 'yolov8n.pt'...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 6.23M/6.23M [00:00<00:00, 170MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/SMART_PARKING-2/train/labels... 1051 images, 6 backgrounds, 0 corrupt: 100%|██████████| 1051/1051 [00:00<00:00, 1165.43it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/SMART_PARKING-2/train/labels.cache\n",
            "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 17514, len(boxes) = 25178. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/SMART_PARKING-2/valid/labels... 288 images, 1 backgrounds, 0 corrupt: 100%|██████████| 288/288 [00:00<00:00, 625.76it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/SMART_PARKING-2/valid/labels.cache\n",
            "WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = 4767, len(boxes) = 6643. To resolve this only boxes will be used and all segments will be removed. To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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=0.001667, momentum=0.9) with parameter groups 154 weight(decay=0.0), 161 weight(decay=0.0005), 160 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 40 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "       1/40      11.6G      3.466      2.714      3.929        443        640: 100%|██████████| 66/66 [01:04<00:00,  1.02it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:08<00:00,  1.06it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643    0.00269     0.0422    0.00156   0.000494\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": [
            "       2/40      12.2G      2.277      1.615      2.713        493        640: 100%|██████████| 66/66 [00:52<00:00,  1.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.34it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.481      0.605      0.435      0.201\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": [
            "       3/40      12.3G      1.872      1.317      2.222        465        640: 100%|██████████| 66/66 [00:52<00:00,  1.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.37it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.624      0.682      0.583      0.361\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": [
            "       4/40      12.3G      1.624       1.08      1.976        280        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.38it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.925       0.75      0.824      0.508\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": [
            "       5/40      12.3G      1.461     0.9852      1.819        286        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.951      0.757      0.847      0.514\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": [
            "       6/40      11.7G      1.283     0.8372      1.668        271        640: 100%|██████████| 66/66 [00:52<00:00,  1.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.939      0.781      0.858      0.599\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": [
            "       7/40        12G      1.203      0.798      1.616        322        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.906      0.817      0.884      0.638\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": [
            "       8/40      12.2G      1.099     0.7372      1.536        291        640: 100%|██████████| 66/66 [00:52<00:00,  1.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.889      0.822      0.888      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": [
            "       9/40      12.3G      1.064      0.705      1.488        396        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.947      0.814      0.883      0.647\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": [
            "      10/40      12.5G      1.063     0.6902      1.489        281        640: 100%|██████████| 66/66 [00:55<00:00,  1.19it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.41it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.949      0.812      0.894      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": [
            "      11/40      12.5G     0.9393     0.6245      1.397        551        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.909      0.839      0.901      0.696\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": [
            "      12/40      12.1G     0.9202     0.6102      1.384        410        640: 100%|██████████| 66/66 [00:52<00:00,  1.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.43it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.941      0.851      0.922      0.694\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": [
            "      13/40      11.9G     0.8874     0.5876      1.362        375        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.933      0.858      0.922      0.723\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": [
            "      14/40      12.3G     0.8376     0.5613      1.318        480        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.45it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.925      0.857      0.925       0.73\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": [
            "      15/40      12.3G     0.8337     0.5563      1.309        193        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.40it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.946      0.844       0.93      0.728\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": [
            "      16/40      12.5G     0.8006     0.5348      1.278        562        640: 100%|██████████| 66/66 [00:52<00:00,  1.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.95      0.852       0.93      0.749\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": [
            "      17/40      12.3G     0.7989      0.526      1.281        408        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.45it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.935       0.87      0.933      0.751\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": [
            "      18/40      12.1G     0.7991     0.5267      1.292        442        640: 100%|██████████| 66/66 [00:52<00:00,  1.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.948      0.876      0.939      0.755\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": [
            "      19/40      11.9G     0.8073     0.5253      1.286        383        640: 100%|██████████| 66/66 [00:52<00:00,  1.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.947      0.872      0.931      0.756\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": [
            "      20/40      12.3G     0.7468     0.4948      1.243        550        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.949      0.871       0.94      0.766\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": [
            "      21/40      11.9G     0.6751     0.4624      1.197        275        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.48it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.942      0.884      0.941      0.777\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": [
            "      22/40      12.1G     0.7572      0.494      1.242        331        640: 100%|██████████| 66/66 [00:52<00:00,  1.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.53it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.941      0.882       0.94      0.773\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": [
            "      23/40      12.3G     0.7346     0.4831      1.234        656        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.95      0.878      0.938      0.777\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": [
            "      24/40      12.2G     0.7227     0.4722      1.229        377        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.42it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.934      0.899      0.943      0.788\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": [
            "      25/40      12.4G     0.6697     0.4434       1.18        267        640: 100%|██████████| 66/66 [00:52<00:00,  1.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.52it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.947      0.898      0.948      0.796\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": [
            "      26/40      12.4G     0.7183     0.4547      1.216        359        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.49it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.937      0.892      0.942      0.796\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": [
            "      27/40      12.3G     0.6738     0.4476      1.183        369        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.947      0.893      0.946      0.806\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": [
            "      28/40      12.3G     0.6582     0.4452       1.18        251        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.944      0.903      0.949      0.804\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": [
            "      29/40      12.3G     0.6689     0.4379      1.194        514        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.945        0.9      0.949        0.8\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": [
            "      30/40      11.9G     0.6712      0.442      1.201        444        640: 100%|██████████| 66/66 [00:52<00:00,  1.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.43it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.942      0.896      0.947      0.799\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": [
            "os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\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": [
            "      31/40      11.7G     0.6461     0.4263      1.206        163        640: 100%|██████████| 66/66 [00:58<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.51it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.949      0.894      0.948      0.795\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": [
            "      32/40      11.6G     0.6437     0.4349      1.221        368        640: 100%|██████████| 66/66 [00:51<00:00,  1.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.938      0.909       0.95      0.803\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": [
            "      33/40      11.7G      0.615     0.4114      1.185        383        640: 100%|██████████| 66/66 [00:51<00:00,  1.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.47it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.948      0.908      0.955      0.812\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": [
            "      34/40      11.4G      0.585     0.3907      1.176        305        640: 100%|██████████| 66/66 [00:51<00:00,  1.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.95      0.906      0.953      0.806\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"
          ]
        },
        {
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          "text": [
            "      35/40      11.6G     0.5804     0.3776      1.149        313        640: 100%|██████████| 66/66 [00:51<00:00,  1.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.954      0.919      0.957      0.819\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": [
            "      36/40      11.6G       0.55     0.3652      1.135        195        640: 100%|██████████| 66/66 [00:51<00:00,  1.29it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.47it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.943      0.917      0.958      0.824\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"
          ]
        },
        {
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          "text": [
            "      37/40      11.6G     0.5654     0.3739      1.156        272        640: 100%|██████████| 66/66 [00:51<00:00,  1.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.45it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.942       0.92      0.956      0.824\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": [
            "      38/40      11.4G     0.5476     0.3643      1.148        373        640: 100%|██████████| 66/66 [00:51<00:00,  1.29it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.946      0.927      0.962      0.829\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": [
            "      39/40      11.6G     0.5302     0.3476      1.124        249        640: 100%|██████████| 66/66 [00:51<00:00,  1.29it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:06<00:00,  1.44it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.951      0.925      0.962      0.831\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": [
            "      40/40      11.6G     0.4957     0.3306      1.092        238        640: 100%|██████████| 66/66 [00:51<00:00,  1.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.54it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.952      0.924      0.962      0.833\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "40 epochs completed in 0.689 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 51.6MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 51.6MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics YOLOv8.1.46 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv9c summary (fused): 384 layers, 25320790 parameters, 0 gradients, 102.3 GFLOPs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:14<00:00,  1.58s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.952      0.924      0.962      0.833\n",
            "                   Car        288       4799      0.974      0.952      0.976      0.877\n",
            "                Vacant        288       1844      0.931      0.896      0.948      0.789\n",
            "Speed: 0.3ms preprocess, 15.9ms inference, 0.0ms loss, 3.1ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ultralytics.utils.metrics.DetMetrics object with attributes:\n",
              "\n",
              "ap_class_index: array([0, 1])\n",
              "box: ultralytics.utils.metrics.Metric object\n",
              "confusion_matrix: <ultralytics.utils.metrics.ConfusionMatrix object at 0x7f738cfee350>\n",
              "curves: ['Precision-Recall(B)', 'F1-Confidence(B)', 'Precision-Confidence(B)', 'Recall-Confidence(B)']\n",
              "curves_results: [[array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[          1,           1,           1, ...,    0.031226,    0.015613,           0],\n",
              "       [          1,           1,           1, ...,    0.010206,   0.0051032,           0]]), 'Recall', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.43908,     0.43908,     0.58395, ...,           0,           0,           0],\n",
              "       [    0.21633,     0.21633,     0.30151, ...,           0,           0,           0]]), 'Confidence', 'F1'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.28277,     0.28277,     0.41592, ...,           1,           1,           1],\n",
              "       [    0.12165,     0.12165,     0.17829, ...,           1,           1,           1]]), 'Confidence', 'Precision'], [array([          0,    0.001001,    0.002002,    0.003003,    0.004004,    0.005005,    0.006006,    0.007007,    0.008008,    0.009009,     0.01001,    0.011011,    0.012012,    0.013013,    0.014014,    0.015015,    0.016016,    0.017017,    0.018018,    0.019019,     0.02002,    0.021021,    0.022022,    0.023023,\n",
              "          0.024024,    0.025025,    0.026026,    0.027027,    0.028028,    0.029029,     0.03003,    0.031031,    0.032032,    0.033033,    0.034034,    0.035035,    0.036036,    0.037037,    0.038038,    0.039039,     0.04004,    0.041041,    0.042042,    0.043043,    0.044044,    0.045045,    0.046046,    0.047047,\n",
              "          0.048048,    0.049049,     0.05005,    0.051051,    0.052052,    0.053053,    0.054054,    0.055055,    0.056056,    0.057057,    0.058058,    0.059059,     0.06006,    0.061061,    0.062062,    0.063063,    0.064064,    0.065065,    0.066066,    0.067067,    0.068068,    0.069069,     0.07007,    0.071071,\n",
              "          0.072072,    0.073073,    0.074074,    0.075075,    0.076076,    0.077077,    0.078078,    0.079079,     0.08008,    0.081081,    0.082082,    0.083083,    0.084084,    0.085085,    0.086086,    0.087087,    0.088088,    0.089089,     0.09009,    0.091091,    0.092092,    0.093093,    0.094094,    0.095095,\n",
              "          0.096096,    0.097097,    0.098098,    0.099099,      0.1001,      0.1011,      0.1021,      0.1031,      0.1041,     0.10511,     0.10611,     0.10711,     0.10811,     0.10911,     0.11011,     0.11111,     0.11211,     0.11311,     0.11411,     0.11512,     0.11612,     0.11712,     0.11812,     0.11912,\n",
              "           0.12012,     0.12112,     0.12212,     0.12312,     0.12412,     0.12513,     0.12613,     0.12713,     0.12813,     0.12913,     0.13013,     0.13113,     0.13213,     0.13313,     0.13413,     0.13514,     0.13614,     0.13714,     0.13814,     0.13914,     0.14014,     0.14114,     0.14214,     0.14314,\n",
              "           0.14414,     0.14515,     0.14615,     0.14715,     0.14815,     0.14915,     0.15015,     0.15115,     0.15215,     0.15315,     0.15415,     0.15516,     0.15616,     0.15716,     0.15816,     0.15916,     0.16016,     0.16116,     0.16216,     0.16316,     0.16416,     0.16517,     0.16617,     0.16717,\n",
              "           0.16817,     0.16917,     0.17017,     0.17117,     0.17217,     0.17317,     0.17417,     0.17518,     0.17618,     0.17718,     0.17818,     0.17918,     0.18018,     0.18118,     0.18218,     0.18318,     0.18418,     0.18519,     0.18619,     0.18719,     0.18819,     0.18919,     0.19019,     0.19119,\n",
              "           0.19219,     0.19319,     0.19419,      0.1952,      0.1962,      0.1972,      0.1982,      0.1992,      0.2002,      0.2012,      0.2022,      0.2032,      0.2042,     0.20521,     0.20621,     0.20721,     0.20821,     0.20921,     0.21021,     0.21121,     0.21221,     0.21321,     0.21421,     0.21522,\n",
              "           0.21622,     0.21722,     0.21822,     0.21922,     0.22022,     0.22122,     0.22222,     0.22322,     0.22422,     0.22523,     0.22623,     0.22723,     0.22823,     0.22923,     0.23023,     0.23123,     0.23223,     0.23323,     0.23423,     0.23524,     0.23624,     0.23724,     0.23824,     0.23924,\n",
              "           0.24024,     0.24124,     0.24224,     0.24324,     0.24424,     0.24525,     0.24625,     0.24725,     0.24825,     0.24925,     0.25025,     0.25125,     0.25225,     0.25325,     0.25425,     0.25526,     0.25626,     0.25726,     0.25826,     0.25926,     0.26026,     0.26126,     0.26226,     0.26326,\n",
              "           0.26426,     0.26527,     0.26627,     0.26727,     0.26827,     0.26927,     0.27027,     0.27127,     0.27227,     0.27327,     0.27427,     0.27528,     0.27628,     0.27728,     0.27828,     0.27928,     0.28028,     0.28128,     0.28228,     0.28328,     0.28428,     0.28529,     0.28629,     0.28729,\n",
              "           0.28829,     0.28929,     0.29029,     0.29129,     0.29229,     0.29329,     0.29429,      0.2953,      0.2963,      0.2973,      0.2983,      0.2993,      0.3003,      0.3013,      0.3023,      0.3033,      0.3043,     0.30531,     0.30631,     0.30731,     0.30831,     0.30931,     0.31031,     0.31131,\n",
              "           0.31231,     0.31331,     0.31431,     0.31532,     0.31632,     0.31732,     0.31832,     0.31932,     0.32032,     0.32132,     0.32232,     0.32332,     0.32432,     0.32533,     0.32633,     0.32733,     0.32833,     0.32933,     0.33033,     0.33133,     0.33233,     0.33333,     0.33433,     0.33534,\n",
              "           0.33634,     0.33734,     0.33834,     0.33934,     0.34034,     0.34134,     0.34234,     0.34334,     0.34434,     0.34535,     0.34635,     0.34735,     0.34835,     0.34935,     0.35035,     0.35135,     0.35235,     0.35335,     0.35435,     0.35536,     0.35636,     0.35736,     0.35836,     0.35936,\n",
              "           0.36036,     0.36136,     0.36236,     0.36336,     0.36436,     0.36537,     0.36637,     0.36737,     0.36837,     0.36937,     0.37037,     0.37137,     0.37237,     0.37337,     0.37437,     0.37538,     0.37638,     0.37738,     0.37838,     0.37938,     0.38038,     0.38138,     0.38238,     0.38338,\n",
              "           0.38438,     0.38539,     0.38639,     0.38739,     0.38839,     0.38939,     0.39039,     0.39139,     0.39239,     0.39339,     0.39439,      0.3954,      0.3964,      0.3974,      0.3984,      0.3994,      0.4004,      0.4014,      0.4024,      0.4034,      0.4044,     0.40541,     0.40641,     0.40741,\n",
              "           0.40841,     0.40941,     0.41041,     0.41141,     0.41241,     0.41341,     0.41441,     0.41542,     0.41642,     0.41742,     0.41842,     0.41942,     0.42042,     0.42142,     0.42242,     0.42342,     0.42442,     0.42543,     0.42643,     0.42743,     0.42843,     0.42943,     0.43043,     0.43143,\n",
              "           0.43243,     0.43343,     0.43443,     0.43544,     0.43644,     0.43744,     0.43844,     0.43944,     0.44044,     0.44144,     0.44244,     0.44344,     0.44444,     0.44545,     0.44645,     0.44745,     0.44845,     0.44945,     0.45045,     0.45145,     0.45245,     0.45345,     0.45445,     0.45546,\n",
              "           0.45646,     0.45746,     0.45846,     0.45946,     0.46046,     0.46146,     0.46246,     0.46346,     0.46446,     0.46547,     0.46647,     0.46747,     0.46847,     0.46947,     0.47047,     0.47147,     0.47247,     0.47347,     0.47447,     0.47548,     0.47648,     0.47748,     0.47848,     0.47948,\n",
              "           0.48048,     0.48148,     0.48248,     0.48348,     0.48448,     0.48549,     0.48649,     0.48749,     0.48849,     0.48949,     0.49049,     0.49149,     0.49249,     0.49349,     0.49449,      0.4955,      0.4965,      0.4975,      0.4985,      0.4995,      0.5005,      0.5015,      0.5025,      0.5035,\n",
              "            0.5045,     0.50551,     0.50651,     0.50751,     0.50851,     0.50951,     0.51051,     0.51151,     0.51251,     0.51351,     0.51451,     0.51552,     0.51652,     0.51752,     0.51852,     0.51952,     0.52052,     0.52152,     0.52252,     0.52352,     0.52452,     0.52553,     0.52653,     0.52753,\n",
              "           0.52853,     0.52953,     0.53053,     0.53153,     0.53253,     0.53353,     0.53453,     0.53554,     0.53654,     0.53754,     0.53854,     0.53954,     0.54054,     0.54154,     0.54254,     0.54354,     0.54454,     0.54555,     0.54655,     0.54755,     0.54855,     0.54955,     0.55055,     0.55155,\n",
              "           0.55255,     0.55355,     0.55455,     0.55556,     0.55656,     0.55756,     0.55856,     0.55956,     0.56056,     0.56156,     0.56256,     0.56356,     0.56456,     0.56557,     0.56657,     0.56757,     0.56857,     0.56957,     0.57057,     0.57157,     0.57257,     0.57357,     0.57457,     0.57558,\n",
              "           0.57658,     0.57758,     0.57858,     0.57958,     0.58058,     0.58158,     0.58258,     0.58358,     0.58458,     0.58559,     0.58659,     0.58759,     0.58859,     0.58959,     0.59059,     0.59159,     0.59259,     0.59359,     0.59459,      0.5956,      0.5966,      0.5976,      0.5986,      0.5996,\n",
              "            0.6006,      0.6016,      0.6026,      0.6036,      0.6046,     0.60561,     0.60661,     0.60761,     0.60861,     0.60961,     0.61061,     0.61161,     0.61261,     0.61361,     0.61461,     0.61562,     0.61662,     0.61762,     0.61862,     0.61962,     0.62062,     0.62162,     0.62262,     0.62362,\n",
              "           0.62462,     0.62563,     0.62663,     0.62763,     0.62863,     0.62963,     0.63063,     0.63163,     0.63263,     0.63363,     0.63463,     0.63564,     0.63664,     0.63764,     0.63864,     0.63964,     0.64064,     0.64164,     0.64264,     0.64364,     0.64464,     0.64565,     0.64665,     0.64765,\n",
              "           0.64865,     0.64965,     0.65065,     0.65165,     0.65265,     0.65365,     0.65465,     0.65566,     0.65666,     0.65766,     0.65866,     0.65966,     0.66066,     0.66166,     0.66266,     0.66366,     0.66466,     0.66567,     0.66667,     0.66767,     0.66867,     0.66967,     0.67067,     0.67167,\n",
              "           0.67267,     0.67367,     0.67467,     0.67568,     0.67668,     0.67768,     0.67868,     0.67968,     0.68068,     0.68168,     0.68268,     0.68368,     0.68468,     0.68569,     0.68669,     0.68769,     0.68869,     0.68969,     0.69069,     0.69169,     0.69269,     0.69369,     0.69469,      0.6957,\n",
              "            0.6967,      0.6977,      0.6987,      0.6997,      0.7007,      0.7017,      0.7027,      0.7037,      0.7047,     0.70571,     0.70671,     0.70771,     0.70871,     0.70971,     0.71071,     0.71171,     0.71271,     0.71371,     0.71471,     0.71572,     0.71672,     0.71772,     0.71872,     0.71972,\n",
              "           0.72072,     0.72172,     0.72272,     0.72372,     0.72472,     0.72573,     0.72673,     0.72773,     0.72873,     0.72973,     0.73073,     0.73173,     0.73273,     0.73373,     0.73473,     0.73574,     0.73674,     0.73774,     0.73874,     0.73974,     0.74074,     0.74174,     0.74274,     0.74374,\n",
              "           0.74474,     0.74575,     0.74675,     0.74775,     0.74875,     0.74975,     0.75075,     0.75175,     0.75275,     0.75375,     0.75475,     0.75576,     0.75676,     0.75776,     0.75876,     0.75976,     0.76076,     0.76176,     0.76276,     0.76376,     0.76476,     0.76577,     0.76677,     0.76777,\n",
              "           0.76877,     0.76977,     0.77077,     0.77177,     0.77277,     0.77377,     0.77477,     0.77578,     0.77678,     0.77778,     0.77878,     0.77978,     0.78078,     0.78178,     0.78278,     0.78378,     0.78478,     0.78579,     0.78679,     0.78779,     0.78879,     0.78979,     0.79079,     0.79179,\n",
              "           0.79279,     0.79379,     0.79479,      0.7958,      0.7968,      0.7978,      0.7988,      0.7998,      0.8008,      0.8018,      0.8028,      0.8038,      0.8048,     0.80581,     0.80681,     0.80781,     0.80881,     0.80981,     0.81081,     0.81181,     0.81281,     0.81381,     0.81481,     0.81582,\n",
              "           0.81682,     0.81782,     0.81882,     0.81982,     0.82082,     0.82182,     0.82282,     0.82382,     0.82482,     0.82583,     0.82683,     0.82783,     0.82883,     0.82983,     0.83083,     0.83183,     0.83283,     0.83383,     0.83483,     0.83584,     0.83684,     0.83784,     0.83884,     0.83984,\n",
              "           0.84084,     0.84184,     0.84284,     0.84384,     0.84484,     0.84585,     0.84685,     0.84785,     0.84885,     0.84985,     0.85085,     0.85185,     0.85285,     0.85385,     0.85485,     0.85586,     0.85686,     0.85786,     0.85886,     0.85986,     0.86086,     0.86186,     0.86286,     0.86386,\n",
              "           0.86486,     0.86587,     0.86687,     0.86787,     0.86887,     0.86987,     0.87087,     0.87187,     0.87287,     0.87387,     0.87487,     0.87588,     0.87688,     0.87788,     0.87888,     0.87988,     0.88088,     0.88188,     0.88288,     0.88388,     0.88488,     0.88589,     0.88689,     0.88789,\n",
              "           0.88889,     0.88989,     0.89089,     0.89189,     0.89289,     0.89389,     0.89489,      0.8959,      0.8969,      0.8979,      0.8989,      0.8999,      0.9009,      0.9019,      0.9029,      0.9039,      0.9049,     0.90591,     0.90691,     0.90791,     0.90891,     0.90991,     0.91091,     0.91191,\n",
              "           0.91291,     0.91391,     0.91491,     0.91592,     0.91692,     0.91792,     0.91892,     0.91992,     0.92092,     0.92192,     0.92292,     0.92392,     0.92492,     0.92593,     0.92693,     0.92793,     0.92893,     0.92993,     0.93093,     0.93193,     0.93293,     0.93393,     0.93493,     0.93594,\n",
              "           0.93694,     0.93794,     0.93894,     0.93994,     0.94094,     0.94194,     0.94294,     0.94394,     0.94494,     0.94595,     0.94695,     0.94795,     0.94895,     0.94995,     0.95095,     0.95195,     0.95295,     0.95395,     0.95495,     0.95596,     0.95696,     0.95796,     0.95896,     0.95996,\n",
              "           0.96096,     0.96196,     0.96296,     0.96396,     0.96496,     0.96597,     0.96697,     0.96797,     0.96897,     0.96997,     0.97097,     0.97197,     0.97297,     0.97397,     0.97497,     0.97598,     0.97698,     0.97798,     0.97898,     0.97998,     0.98098,     0.98198,     0.98298,     0.98398,\n",
              "           0.98498,     0.98599,     0.98699,     0.98799,     0.98899,     0.98999,     0.99099,     0.99199,     0.99299,     0.99399,     0.99499,       0.996,       0.997,       0.998,       0.999,           1]), array([[    0.98187,     0.98187,     0.97979, ...,           0,           0,           0],\n",
              "       [    0.97614,     0.97614,     0.97614, ...,           0,           0,           0]]), 'Confidence', 'Recall']]\n",
              "fitness: 0.8458503164247795\n",
              "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
              "maps: array([    0.87679,     0.78908])\n",
              "names: {0: 'Car', 1: 'Vacant'}\n",
              "plot: True\n",
              "results_dict: {'metrics/precision(B)': 0.9521270094747201, 'metrics/recall(B)': 0.9242470864546527, 'metrics/mAP50(B)': 0.9621172076463955, 'metrics/mAP50-95(B)': 0.832931772955711, 'fitness': 0.8458503164247795}\n",
              "save_dir: PosixPath('runs/detect/train')\n",
              "speed: {'preprocess': 0.26088953018188477, 'inference': 15.859068267875246, 'loss': 0.025527344809638128, 'postprocess': 3.1350155671437583}\n",
              "task: 'detect'"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Oqg4i0W8CL3t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "QbLLLO7WC0kx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = YOLO('/content/runs/detect/train/weights/best.pt')\n",
        "\n",
        "# Load the training metrics\n",
        "metrics = pd.read_csv('/content/runs/detect/train/results.csv')\n",
        "\n",
        "# Get the final epoch metrics\n",
        "final_metrics = metrics.iloc[-1]\n"
      ],
      "metadata": {
        "id": "CBeHqbEmDD2T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(final_metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ap3Dc2TDfYc",
        "outputId": "daa3b256-bc54-4309-93e5-f97c23404fb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  epoch    40.000000\n",
            "         train/box_loss     0.495670\n",
            "         train/cls_loss     0.330640\n",
            "         train/dfl_loss     1.092300\n",
            "   metrics/precision(B)     0.952260\n",
            "      metrics/recall(B)     0.923850\n",
            "       metrics/mAP50(B)     0.962120\n",
            "    metrics/mAP50-95(B)     0.832860\n",
            "           val/box_loss     1.046900\n",
            "           val/cls_loss     0.768730\n",
            "           val/dfl_loss     1.698700\n",
            "                 lr/pg0     0.000058\n",
            "                 lr/pg1     0.000058\n",
            "                 lr/pg2     0.000058\n",
            "Name: 39, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns = final_metrics.index\n",
        "for col in columns:\n",
        "    if 'accuracy' in col:\n",
        "        print(f\"Accuracy: {final_metrics[col]:.4f}\")\n",
        "    elif 'box_loss' in col:\n",
        "        print(f\"Box Loss: {final_metrics[col]:.4f}\")\n",
        "    elif 'cls_loss' in col:\n",
        "        print(f\"Class Loss: {final_metrics[col]:.4f}\")\n",
        "    elif 'obj_loss' in col:\n",
        "        print(f\"Object Loss: {final_metrics[col]:.4f}\")\n",
        "\n",
        "# Plot the mAP graph\n",
        "mAP_cols = [col for col in metrics.columns if 'mAP' in col]\n",
        "plt.figure(figsize=(10, 6))\n",
        "for col in mAP_cols:\n",
        "    plt.plot(metrics[col], label=col)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('mAP')\n",
        "plt.title('mAP Plot')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "0QT4JZLRDnVT",
        "outputId": "e5575b91-92de-4d5a-8273-018d56db1f82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Box Loss: 0.4957\n",
            "Class Loss: 0.3306\n",
            "Box Loss: 1.0469\n",
            "Class Loss: 0.7687\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "box_loss_col = [col for col in metrics.columns if 'box_loss' in col]\n",
        "plt.figure(figsize=(10, 6))\n",
        "for col in box_loss_col:\n",
        "    plt.plot(metrics[col], label=col)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Box Loss')\n",
        "plt.title('Box Loss vs Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "qBhehakZDqWU",
        "outputId": "d62c934a-f3d5-459a-fdaa-140e07a34079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cls_loss_col = [col for col in metrics.columns if 'cls_loss' in col]\n",
        "plt.figure(figsize=(10, 6))\n",
        "for col in cls_loss_col:\n",
        "    plt.plot(metrics[col], label=col)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Class Loss')\n",
        "plt.title('Class Loss vs Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "LhFWHZDXDsVQ",
        "outputId": "a3e0130a-b557-426f-fb41-2f749477a522"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mAP_cols = [col for col in metrics.columns if 'mAP' in col]\n",
        "plt.figure(figsize=(10, 6))\n",
        "for col in mAP_cols:\n",
        "    plt.plot(metrics[col], label=col)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('mAP')\n",
        "plt.title('mAP vs Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "6qRvBXqbDvDN",
        "outputId": "aa1228e5-9030-4c67-ee3e-d2924dcdfa5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "precision_col = [col for col in metrics.columns if 'precision' in col]\n",
        "plt.figure(figsize=(10, 6))\n",
        "for col in precision_col:\n",
        "    plt.plot(metrics[col], label=col)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision vs Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "tv2AZGgxDxIj",
        "outputId": "e2a73b75-1ec5-40a8-e84b-95ffe8c4ea33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recall_col = [col for col in metrics.columns if 'recall' in col]\n",
        "plt.figure(figsize=(10, 6))\n",
        "for col in recall_col:\n",
        "    plt.plot(metrics[col], label=col)\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Recall')\n",
        "plt.title('Recall vs Epochs')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "hkxT-iDbD5iL",
        "outputId": "b60b8714-b95b-49f5-879a-2a1e10a74fb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}