yolov6.ipynb

{
  "nbformat": 4,
  "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": "xPt7X-NPHBX-",
        "outputId": "09fd47d0-bd33-4113-f944-d61fce1175e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.27)\n",
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          ]
        }
      ],
      "source": [
        "!pip install roboflow"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ultralytics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "safpLN-QHB-T",
        "outputId": "f0ac0977-90ba-43bb-cc1a-8f0ad1c1da76"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.2.0-py3-none-any.whl (750 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m750.8/750.8 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "Collecting thop>=0.1.1 (from ultralytics)\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",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch>=1.8.0->ultralytics)\n",
            "  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",
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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-8.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# load dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rl4ph8TIHU_q",
        "outputId": "05b59570-ad69-4626-f2c4-6d4c1cc0036d"
      },
      "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 mt-yolov6:: 100%|██████████| 124933/124933 [00:01<00:00, 64731.75it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to SMART_PARKING-2 in mt-yolov6:: 100%|██████████| 2913/2913 [00:00<00:00, 3099.68it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO"
      ],
      "metadata": {
        "id": "BVuQ4BOGHpPY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Build a YOLOv6n model from scratch\n",
        "model = YOLO('yolov6n.yaml')"
      ],
      "metadata": {
        "id": "lmR2qOP5Hr1S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Train the model on the COCO8 example dataset for 100 epochs\n",
        "results = model.train (data='/content/SMART_PARKING-2/data.yaml', epochs=40, imgsz=640)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xORWcn4bH5Df",
        "outputId": "2fc14afd-c4fb-4082-979d-0e753f5115e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics YOLOv8.2.0 🚀 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=yolov6n.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, 17.4MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overriding model.yaml nc=80 with nc=2\n",
            "\u001b[34m\u001b[1mactivation:\u001b[0m nn.ReLU()\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
            "  2                  -1  2     18560  ultralytics.nn.modules.conv.Conv             [32, 32, 3, 1]                \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
            "  4                  -1  4    147968  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  6                  -1  6    886272  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 1]              \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  8                  -1  2   1180672  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 1]              \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
            " 10                  -1  1     16512  ultralytics.nn.modules.conv.Conv             [256, 64, 1, 1]               \n",
            " 11                  -1  1     16448  torch.nn.modules.conv.ConvTranspose2d        [64, 64, 2, 2, 0]             \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  1    110720  ultralytics.nn.modules.conv.Conv             [192, 64, 3, 1]               \n",
            " 14                  -1  3    110976  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                \n",
            " 15                  -1  1      2112  ultralytics.nn.modules.conv.Conv             [64, 32, 1, 1]                \n",
            " 16                  -1  1      4128  torch.nn.modules.conv.ConvTranspose2d        [32, 32, 2, 2, 0]             \n",
            " 17             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  1     27712  ultralytics.nn.modules.conv.Conv             [96, 32, 3, 1]                \n",
            " 19                  -1  3     27840  ultralytics.nn.modules.conv.Conv             [32, 32, 3, 1]                \n",
            " 20                  -1  1      9280  ultralytics.nn.modules.conv.Conv             [32, 32, 3, 2]                \n",
            " 21            [-1, 15]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 22                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                \n",
            " 23                  -1  3    110976  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                \n",
            " 24                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            " 25            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 26                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 1]              \n",
            " 27                  -1  3    443136  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 1]              \n",
            " 28        [19, 23, 27]  1    345622  ultralytics.nn.modules.head.Detect           [2, [32, 64, 128]]            \n",
            "YOLOv6n summary: 195 layers, 4238342 parameters, 4238326 gradients, 11.9 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
            "Freezing layer 'model.28.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, 76.8MB/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/labels/train... 1051 images, 6 backgrounds, 0 corrupt: 100%|██████████| 1051/1051 [00:01<00:00, 623.71it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/SMART_PARKING-2/labels/train.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",
            "\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/labels/valid... 288 images, 1 backgrounds, 0 corrupt: 100%|██████████| 288/288 [00:00<00:00, 322.30it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/SMART_PARKING-2/labels/valid.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",
            "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 53 weight(decay=0.0), 62 weight(decay=0.0005), 61 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      2.36G      3.446      3.203      3.894        443        640: 100%|██████████| 66/66 [00:43<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.82it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643     0.0031     0.0561    0.00198   0.000641\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      3.25G      2.615      2.126      2.945        493        640: 100%|██████████| 66/66 [00:29<00:00,  2.20it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.34it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643     0.0157      0.255     0.0481     0.0191\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      2.97G      2.067      1.544      2.309        465        640: 100%|██████████| 66/66 [00:27<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.22it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.502      0.571      0.436      0.214\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      3.26G      1.871      1.354      2.092        280        640: 100%|██████████| 66/66 [00:28<00:00,  2.35it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.792       0.72      0.752      0.421\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      2.77G      1.762      1.272      1.964        286        640: 100%|██████████| 66/66 [00:28<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.03it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.812      0.607      0.706      0.402\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      2.44G      1.584      1.117      1.792        271        640: 100%|██████████| 66/66 [00:28<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.13it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.953      0.726      0.836      0.536\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      3.41G      1.446      1.058      1.708        322        640: 100%|██████████| 66/66 [00:27<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.94it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.914      0.721      0.823      0.544\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      3.09G      1.355     0.9955      1.627        291        640: 100%|██████████| 66/66 [00:28<00:00,  2.30it/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      0.888      0.756      0.849      0.562\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      2.57G      1.303      0.949      1.571        396        640: 100%|██████████| 66/66 [00:27<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:07<00:00,  1.24it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.924      0.773      0.868      0.582\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      2.74G       1.31     0.9526      1.582        281        640: 100%|██████████| 66/66 [00:28<00:00,  2.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.72it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.926      0.808      0.885      0.622\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      11/40      3.62G      1.167     0.8632      1.477        551        640: 100%|██████████| 66/66 [00:27<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.923      0.762      0.861      0.585\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      2.83G      1.149     0.8348      1.458        410        640: 100%|██████████| 66/66 [00:28<00:00,  2.31it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.80it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.899      0.801      0.885      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": [
            "      13/40      2.89G      1.118     0.8169      1.436        375        640: 100%|██████████| 66/66 [00:28<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.33it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.903        0.8      0.889      0.656\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      2.96G      1.099     0.7934      1.404        480        640: 100%|██████████| 66/66 [00:27<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.968      0.777      0.894       0.64\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      15/40      3.19G      1.055     0.7692      1.383        193        640: 100%|██████████| 66/66 [00:27<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.94      0.807      0.892      0.616\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      3.03G      1.036     0.7619      1.362        562        640: 100%|██████████| 66/66 [00:27<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.74it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.948      0.812      0.902      0.676\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      17/40      3.12G      1.031     0.7555      1.373        408        640: 100%|██████████| 66/66 [00:27<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.23it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.946       0.79      0.897      0.676\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      18/40      2.74G      1.011     0.7456      1.369        442        640: 100%|██████████| 66/66 [00:27<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.79it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.904      0.829      0.899      0.674\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      2.78G      0.989     0.7256      1.356        383        640: 100%|██████████| 66/66 [00:27<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.25it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.918      0.837      0.911      0.702\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       3.4G     0.9543     0.7017      1.321        550        640: 100%|██████████| 66/66 [00:27<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.93it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.869      0.848      0.905      0.695\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      21/40      3.21G     0.8866     0.6708      1.274        275        640: 100%|██████████| 66/66 [00:27<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.24it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.947      0.824      0.907       0.68\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      3.03G     0.9598     0.6972      1.325        331        640: 100%|██████████| 66/66 [00:27<00:00,  2.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.82it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.929      0.835      0.906      0.698\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      23/40      2.97G     0.9249     0.6831      1.306        656        640: 100%|██████████| 66/66 [00:27<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.964      0.796      0.911       0.71\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      3.03G     0.9353     0.6695      1.297        377        640: 100%|██████████| 66/66 [00:27<00:00,  2.36it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.94it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.962      0.824      0.922      0.725\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      2.94G     0.8588     0.6366       1.25        267        640: 100%|██████████| 66/66 [00:28<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.74it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.895      0.857      0.912      0.708\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       3.1G     0.8972     0.6458      1.286        359        640: 100%|██████████| 66/66 [00:30<00:00,  2.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.86it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.926      0.847      0.921      0.715\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      3.16G      0.861     0.6394      1.254        369        640: 100%|██████████| 66/66 [00:29<00:00,  2.26it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.07it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.935      0.845      0.917      0.723\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "      28/40      2.76G     0.8492     0.6345      1.254        251        640: 100%|██████████| 66/66 [00:29<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.95it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.939      0.844      0.913      0.722\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      3.98G      0.842     0.6222      1.245        514        640: 100%|██████████| 66/66 [00:29<00:00,  2.24it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.63it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.906      0.863      0.922      0.724\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      2.54G     0.8499     0.6247      1.262        444        640: 100%|██████████| 66/66 [00:28<00:00,  2.32it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.21it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.906      0.859      0.911       0.73\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",
            "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       2.1G     0.8107      0.622       1.26        163        640: 100%|██████████| 66/66 [00:36<00:00,  1.82it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.11it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.951      0.827      0.921      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": [
            "      32/40      2.29G     0.8231     0.6402      1.275        368        640: 100%|██████████| 66/66 [00:26<00:00,  2.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  1.97it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.951      0.842      0.918      0.727\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      2.04G     0.7764     0.6027      1.232        383        640: 100%|██████████| 66/66 [00:26<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:04<00:00,  2.01it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.92      0.862      0.926      0.741\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      1.93G     0.7484     0.5791      1.223        305        640: 100%|██████████| 66/66 [00:25<00:00,  2.56it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.28it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.956      0.861      0.934      0.737\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": [
            "      35/40      1.88G      0.754      0.567      1.213        313        640: 100%|██████████| 66/66 [00:25<00:00,  2.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.27it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.96      0.856       0.93      0.754\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      1.87G     0.7198     0.5481      1.202        195        640: 100%|██████████| 66/66 [00:25<00:00,  2.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.75it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.929      0.875      0.931      0.759\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": [
            "      37/40      1.88G     0.7441     0.5639      1.212        272        640: 100%|██████████| 66/66 [00:26<00:00,  2.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.26it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.958      0.866      0.935      0.765\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      1.93G     0.7343     0.5605      1.211        373        640: 100%|██████████| 66/66 [00:25<00:00,  2.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.30it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.957      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": [
            "      39/40      1.88G     0.7111     0.5353      1.184        249        640: 100%|██████████| 66/66 [00:26<00:00,  2.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:05<00:00,  1.77it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643       0.96      0.875      0.938      0.771\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      1.87G     0.6801     0.5113      1.168        238        640: 100%|██████████| 66/66 [00:26<00:00,  2.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:03<00:00,  2.43it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.961      0.869      0.938      0.776\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "40 epochs completed in 0.374 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 8.7MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 8.7MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics YOLOv8.2.0 🚀 Python-3.10.12 torch-2.2.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv6n summary (fused): 142 layers, 4233942 parameters, 0 gradients, 11.8 GFLOPs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 9/9 [00:31<00:00,  3.53s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   all        288       6643      0.961      0.869      0.938      0.776\n",
            "                   Car        288       4799      0.979      0.904      0.964      0.821\n",
            "                Vacant        288       1844      0.942      0.835      0.912       0.73\n",
            "Speed: 0.9ms preprocess, 3.0ms inference, 0.0ms loss, 9.6ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "_-TNCFBLH7P7"
      },
      "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": "ou5Z6SM1JHaU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(final_metrics)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JXN-0dD5Nz73",
        "outputId": "3732c8b4-0ece-466a-ba77-23272f2f136b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                  epoch    40.000000\n",
            "         train/box_loss     0.680140\n",
            "         train/cls_loss     0.511270\n",
            "         train/dfl_loss     1.167800\n",
            "   metrics/precision(B)     0.960570\n",
            "      metrics/recall(B)     0.869460\n",
            "       metrics/mAP50(B)     0.937920\n",
            "    metrics/mAP50-95(B)     0.775680\n",
            "           val/box_loss     0.825610\n",
            "           val/cls_loss     0.671100\n",
            "           val/dfl_loss     1.337400\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": 637
        },
        "id": "eAa3Z64VN_WW",
        "outputId": "97a56b88-7408-4e01-8b23-41ed66b4bf4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Box Loss: 0.6801\n",
            "Class Loss: 0.5113\n",
            "Box Loss: 0.8256\n",
            "Class Loss: 0.6711\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": "KLmT1_GzOBYj",
        "outputId": "e3c0daba-75f1-466e-b3d9-07a7c3b1a827"
      },
      "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": "i4z573DUODku",
        "outputId": "26373181-7f42-4bc8-997b-ae3813c4721d"
      },
      "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": "Qde24zXyOFZO",
        "outputId": "be842022-8526-49d3-f9dc-7fbd574c8c3e"
      },
      "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": "MKWI0WLMOG6_",
        "outputId": "a917d603-ad6f-41c7-eb2e-4439778b2e7b"
      },
      "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": [],
      "metadata": {
        "id": "PZqF8NxOOIYv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}