Exams_Anomalous_Detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/akeren/exams-anomalous-detection/blob/main/Exams_Anomalous_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LcFGF1K9lnvz"
      },
      "source": [
        "# Checking Hardware Accelerator  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaepbEdSuxiJ",
        "outputId": "32115143-d266-4128-db52-8592e90afa28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Sat Dec 28 05:05:59 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lipvamiiv2z2"
      },
      "source": [
        "# Install Roboflow Python SDK to download a YOLOv11 dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdkrssC4wKlE",
        "outputId": "938c87a3-7451-448b-9a5a-ac1f0a81a5f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting roboflow\n",
            "  Downloading roboflow-1.1.50-py3-none-any.whl.metadata (9.7 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from roboflow) (2024.12.14)\n",
            "Collecting idna==3.7 (from roboflow)\n",
            "  Downloading idna-3.7-py3-none-any.whl.metadata (9.9 kB)\n",
            "Requirement already satisfied: cycler in /usr/local/lib/python3.10/dist-packages (from roboflow) (0.12.1)\n",
            "Requirement already satisfied: kiwisolver>=1.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.4.7)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from roboflow) (3.8.0)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.26.4)\n",
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            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from roboflow) (11.0.0)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.8.2)\n",
            "Collecting python-dotenv (from roboflow)\n",
            "  Downloading python_dotenv-1.0.1-py3-none-any.whl.metadata (23 kB)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.32.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.17.0)\n",
            "Requirement already satisfied: urllib3>=1.26.6 in /usr/local/lib/python3.10/dist-packages (from roboflow) (2.2.3)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.10/dist-packages (from roboflow) (4.67.1)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from roboflow) (6.0.2)\n",
            "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.10/dist-packages (from roboflow) (1.0.0)\n",
            "Collecting filetype (from roboflow)\n",
            "  Downloading filetype-1.2.0-py2.py3-none-any.whl.metadata (6.5 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (1.3.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (4.55.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (24.2)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->roboflow) (3.2.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.4.0)\n",
            "Downloading roboflow-1.1.50-py3-none-any.whl (81 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.5/81.5 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading idna-3.7-py3-none-any.whl (66 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.8/66.8 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading filetype-1.2.0-py2.py3-none-any.whl (19 kB)\n",
            "Downloading python_dotenv-1.0.1-py3-none-any.whl (19 kB)\n",
            "Installing collected packages: filetype, python-dotenv, idna, roboflow\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.10\n",
            "    Uninstalling idna-3.10:\n",
            "      Successfully uninstalled idna-3.10\n",
            "Successfully installed filetype-1.2.0 idna-3.7 python-dotenv-1.0.1 roboflow-1.1.50\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading Dataset Version Zip in exam-cheating-2 to yolov11:: 100%|██████████| 20731/20731 [00:00<00:00, 22450.97it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to exam-cheating-2 in yolov11:: 100%|██████████| 736/736 [00:00<00:00, 6514.57it/s]\n"
          ]
        }
      ],
      "source": [
        "# Install the Roboflow package\n",
        "!pip install roboflow\n",
        "\n",
        "# Import Roboflow and Google Colab userdata\n",
        "from roboflow import Roboflow\n",
        "from google.colab import userdata\n",
        "\n",
        "# Fetch the API key securely\n",
        "api_key = userdata.get('ROBO_FLOW_API_KEY')\n",
        "if not api_key:\n",
        "    raise ValueError('API key is missing. Ensure it is saved in Colab userdata.')\n",
        "\n",
        "# Initialize Roboflow\n",
        "rf = Roboflow(api_key=api_key)\n",
        "\n",
        "# Access the workspace and project\n",
        "workspace_name = 'kattal'\n",
        "project_name = 'exam-cheating'\n",
        "project = rf.workspace(workspace_name).project(project_name)\n",
        "\n",
        "# Access the specific version and download\n",
        "version_number = 2\n",
        "version = project.version(version_number)\n",
        "\n",
        "# Ensure the correct model framework\n",
        "framework = \"yolov11\"\n",
        "dataset = version.download(framework)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCk803Hmm8I8"
      },
      "source": [
        "# Install ultralytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diIT58rrnDQV",
        "outputId": "4a33249d-869e-46e9-8b46-446a7fc662b8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.3.55-py3-none-any.whl.metadata (35 kB)\n",
            "Requirement already satisfied: numpy>=1.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.26.4)\n",
            "Requirement already satisfied: matplotlib>=3.3.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (3.8.0)\n",
            "Requirement already satisfied: opencv-python>=4.6.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.10.0.84)\n",
            "Requirement already satisfied: pillow>=7.1.2 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (11.0.0)\n",
            "Requirement already satisfied: pyyaml>=5.3.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (6.0.2)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.32.3)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (1.13.1)\n",
            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.5.1+cu121)\n",
            "Requirement already satisfied: torchvision>=0.9.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.20.1+cu121)\n",
            "Requirement already satisfied: tqdm>=4.64.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (4.67.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from ultralytics) (5.9.5)\n",
            "Requirement already satisfied: py-cpuinfo in /usr/local/lib/python3.10/dist-packages (from ultralytics) (9.0.0)\n",
            "Requirement already satisfied: pandas>=1.1.4 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (2.2.2)\n",
            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from ultralytics) (0.13.2)\n",
            "Collecting ultralytics-thop>=2.0.0 (from ultralytics)\n",
            "  Downloading ultralytics_thop-2.0.13-py3-none-any.whl.metadata (9.4 kB)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (4.55.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (1.4.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (24.2)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (3.2.0)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.3.0->ultralytics) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas>=1.1.4->ultralytics) (2024.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.23.0->ultralytics) (2024.12.14)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.16.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (2024.10.0)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.8.0->ultralytics) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch>=1.8.0->ultralytics) (1.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=3.3.0->ultralytics) (1.17.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.8.0->ultralytics) (3.0.2)\n",
            "Downloading ultralytics-8.3.55-py3-none-any.whl (904 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m904.3/904.3 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ultralytics_thop-2.0.13-py3-none-any.whl (26 kB)\n",
            "Installing collected packages: ultralytics-thop, ultralytics\n",
            "Successfully installed ultralytics-8.3.55 ultralytics-thop-2.0.13\n"
          ]
        }
      ],
      "source": [
        "# Install ultralytices package\n",
        "!pip install ultralytics"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9PXlk5qAnYOP"
      },
      "source": [
        "# Load the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y0HdG3etmOu5",
        "outputId": "f1da5789-dc87-464e-9287-6aace93bf775"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Creating new Ultralytics Settings v0.0.6 file ✅ \n",
            "View Ultralytics Settings with 'yolo settings' or at '/root/.config/Ultralytics/settings.json'\n",
            "Update Settings with 'yolo settings key=value', i.e. 'yolo settings runs_dir=path/to/dir'. For help see https://docs.ultralytics.com/quickstart/#ultralytics-settings.\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5.35M/5.35M [00:00<00:00, 67.2MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Import YOLO\n",
        "from ultralytics import YOLO\n",
        "\n",
        "model = YOLO('yolo11n.pt')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zc7rJ4Iaywc1"
      },
      "source": [
        "# Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZfDdQevryzLH",
        "outputId": "b1772573-2dcd-4051-f53c-5d61c194f7c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Ultralytics 8.3.55 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolo11n.pt, data=/content/exam-cheating-2/data.yaml, epochs=100, time=None, patience=20, batch=32, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=AdamW, 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=True, 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=True, opset=None, workspace=None, nms=False, lr0=0.001, lrf=0.01, momentum=0.937, weight_decay=0.001, 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, 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, copy_paste_mode=flip, 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"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 755k/755k [00:00<00:00, 14.8MB/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Overriding model.yaml nc=80 with nc=3\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                \n",
            "  2                  -1  1      6640  ultralytics.nn.modules.block.C3k2            [32, 64, 1, False, 0.25]      \n",
            "  3                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            "  4                  -1  1     26080  ultralytics.nn.modules.block.C3k2            [64, 128, 1, False, 0.25]     \n",
            "  5                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            "  6                  -1  1     87040  ultralytics.nn.modules.block.C3k2            [128, 128, 1, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  8                  -1  1    346112  ultralytics.nn.modules.block.C3k2            [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 \n",
            " 10                  -1  1    249728  ultralytics.nn.modules.block.C2PSA           [256, 256, 1]                 \n",
            " 11                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 13                  -1  1    111296  ultralytics.nn.modules.block.C3k2            [384, 128, 1, False]          \n",
            " 14                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 15             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 16                  -1  1     32096  ultralytics.nn.modules.block.C3k2            [256, 64, 1, False]           \n",
            " 17                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                \n",
            " 18            [-1, 13]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 19                  -1  1     86720  ultralytics.nn.modules.block.C3k2            [192, 128, 1, False]          \n",
            " 20                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            " 21            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 22                  -1  1    378880  ultralytics.nn.modules.block.C3k2            [384, 256, 1, True]           \n",
            " 23        [16, 19, 22]  1    431257  ultralytics.nn.modules.head.Detect           [3, [64, 128, 256]]           \n",
            "YOLO11n summary: 319 layers, 2,590,425 parameters, 2,590,409 gradients, 6.4 GFLOPs\n",
            "\n",
            "Transferred 448/499 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train', view at http://localhost:6006/\n",
            "Freezing layer 'model.23.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/exam-cheating-2/train/labels... 315 images, 0 backgrounds, 0 corrupt: 100%|██████████| 315/315 [00:00<00:00, 1678.67it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/exam-cheating-2/train/labels.cache\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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "A new version of Albumentations is available: 1.4.24 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1.\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/exam-cheating-2/valid/labels... 30 images, 0 backgrounds, 0 corrupt: 100%|██████████| 30/30 [00:00<00:00, 1200.71it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/exam-cheating-2/valid/labels.cache\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Plotting labels to runs/detect/train/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001, momentum=0.937) with parameter groups 81 weight(decay=0.0), 88 weight(decay=0.001), 87 bias(decay=0.0)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mmodel graph visualization added ✅\n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train\u001b[0m\n",
            "Starting training for 100 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      1/100      5.56G      2.244      3.297      1.862        787        640: 100%|██████████| 10/10 [00:07<00:00,  1.31it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204     0.0033      0.158    0.00207   0.000431\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      2/100      5.17G      1.867      2.387      1.511        686        640: 100%|██████████| 10/10 [00:04<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:03<00:00,  3.44s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204   0.000407     0.0524   0.000476   7.42e-05\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      3/100      5.75G      1.746      2.147      1.438        697        640: 100%|██████████| 10/10 [00:06<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.00it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204     0.0781       0.47     0.0963     0.0269\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      4/100       5.6G      1.664      1.902      1.417        824        640: 100%|██████████| 10/10 [00:04<00:00,  2.02it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204     0.0373      0.821       0.24     0.0948\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      5/100      5.44G      1.619      1.755      1.412        795        640: 100%|██████████| 10/10 [00:05<00:00,  1.74it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.22s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.495       0.42      0.313      0.112\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      6/100       5.8G      1.583      1.688      1.403        771        640: 100%|██████████| 10/10 [00:04<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.42it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.771       0.19      0.458        0.2\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      7/100      5.72G      1.526      1.584      1.347        874        640: 100%|██████████| 10/10 [00:04<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.00s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.58      0.434      0.525      0.281\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      8/100      5.66G      1.476      1.544      1.337        596        640: 100%|██████████| 10/10 [00:05<00:00,  1.85it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.60it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.522      0.518      0.522      0.227\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "      9/100      5.34G      1.459      1.481      1.308        783        640: 100%|██████████| 10/10 [00:03<00:00,  2.57it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.72it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.426       0.53      0.516       0.24\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     10/100      5.55G      1.474      1.455      1.318        869        640: 100%|██████████| 10/10 [00:05<00:00,  1.69it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.22it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.545      0.661      0.657      0.347\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     11/100      5.31G      1.484      1.462      1.325        796        640: 100%|██████████| 10/10 [00:04<00:00,  2.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.02it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.594      0.624      0.671      0.357\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     12/100      5.45G      1.411      1.395      1.289        906        640: 100%|██████████| 10/10 [00:04<00:00,  2.27it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.39it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.657      0.817      0.747      0.428\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     13/100      5.68G      1.419      1.409      1.293        589        640: 100%|██████████| 10/10 [00:05<00:00,  1.98it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.57it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.679       0.78      0.744      0.458\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     14/100      5.84G      1.357      1.372      1.267        801        640: 100%|██████████| 10/10 [00:03<00:00,  2.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.73it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.707      0.697        0.7       0.42\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     15/100      5.52G      1.361      1.325      1.254        636        640: 100%|██████████| 10/10 [00:06<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.14it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.566      0.585      0.616      0.343\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     16/100      6.08G      1.343      1.317      1.253        655        640: 100%|██████████| 10/10 [00:04<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.66it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.662      0.719      0.742      0.404\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     17/100      5.69G      1.312      1.282      1.241        833        640: 100%|██████████| 10/10 [00:04<00:00,  2.05it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.04it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.743      0.639      0.696      0.426\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     18/100      5.21G      1.309      1.279      1.235        653        640: 100%|██████████| 10/10 [00:04<00:00,  2.25it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.30it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.655       0.72      0.763      0.479\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     19/100      6.16G      1.272      1.259      1.223        681        640: 100%|██████████| 10/10 [00:03<00:00,  2.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.96it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.665       0.71      0.761      0.468\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     20/100      5.05G       1.26      1.232       1.21        745        640: 100%|██████████| 10/10 [00:06<00:00,  1.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.06it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.743      0.655      0.759      0.461\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     21/100      5.58G      1.246      1.203      1.206        844        640: 100%|██████████| 10/10 [00:03<00:00,  2.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.97it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.732      0.722      0.767      0.489\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     22/100       5.6G      1.276      1.217       1.22        674        640: 100%|██████████| 10/10 [00:04<00:00,  2.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.22it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.698      0.791      0.774      0.523\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     23/100      5.45G      1.249       1.18      1.202        788        640: 100%|██████████| 10/10 [00:04<00:00,  2.31it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.75it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.774      0.795      0.823      0.539\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     24/100       5.4G      1.229      1.197      1.198        779        640: 100%|██████████| 10/10 [00:04<00:00,  2.35it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.62it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.776      0.815      0.823      0.537\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     25/100      5.95G      1.209      1.157      1.186        823        640: 100%|██████████| 10/10 [00:06<00:00,  1.60it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.87it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.803      0.811      0.844      0.546\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     26/100      5.51G      1.195       1.14      1.173        810        640: 100%|██████████| 10/10 [00:04<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.55it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.779       0.73      0.789      0.517\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     27/100      5.52G      1.184      1.134      1.182        860        640: 100%|██████████| 10/10 [00:05<00:00,  1.79it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.48it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.803      0.792      0.857      0.572\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     28/100         6G       1.18      1.101      1.173        757        640: 100%|██████████| 10/10 [00:04<00:00,  2.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.92it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.803       0.76      0.835      0.539\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     29/100      5.73G      1.166      1.094      1.157        881        640: 100%|██████████| 10/10 [00:04<00:00,  2.28it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.56it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.717      0.806      0.835      0.566\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     30/100      5.54G       1.17      1.109      1.166        720        640: 100%|██████████| 10/10 [00:04<00:00,  2.04it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.22it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.793      0.707      0.812      0.546\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     31/100       5.6G      1.163      1.101      1.163        835        640: 100%|██████████| 10/10 [00:03<00:00,  2.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.68it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.764      0.788      0.805      0.542\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     32/100      5.56G      1.155      1.087      1.155        702        640: 100%|██████████| 10/10 [00:06<00:00,  1.63it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.41it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.785      0.746      0.789      0.544\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     33/100      5.26G       1.14      1.079      1.147        745        640: 100%|██████████| 10/10 [00:04<00:00,  2.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.751      0.831      0.831      0.561\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     34/100      5.41G      1.136      1.064      1.143        654        640: 100%|██████████| 10/10 [00:04<00:00,  2.18it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.32it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.765      0.755      0.819      0.521\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     35/100      5.88G       1.16      1.054      1.156        759        640: 100%|██████████| 10/10 [00:04<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.57it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.754      0.763      0.818      0.539\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     36/100      5.37G      1.134      1.015      1.141        828        640: 100%|██████████| 10/10 [00:04<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.10it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.773       0.81      0.841      0.577\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     37/100      5.71G      1.132      1.032      1.156        792        640: 100%|██████████| 10/10 [00:06<00:00,  1.60it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.83      0.752      0.837      0.598\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     38/100      5.21G      1.101      1.026       1.14        680        640: 100%|██████████| 10/10 [00:04<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.79it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.761      0.787       0.85      0.597\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     39/100      5.55G      1.102      1.017       1.13        842        640: 100%|██████████| 10/10 [00:05<00:00,  1.67it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.796      0.798      0.855      0.606\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     40/100      5.64G      1.106     0.9996      1.134        698        640: 100%|██████████| 10/10 [00:04<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.845      0.821      0.874      0.605\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     41/100      5.55G      1.091      1.005       1.13        645        640: 100%|██████████| 10/10 [00:04<00:00,  2.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.33it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.811      0.828      0.865      0.613\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     42/100      5.45G      1.097     0.9894      1.127        763        640: 100%|██████████| 10/10 [00:05<00:00,  1.81it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.11it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.846      0.856      0.885      0.611\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     43/100      5.24G      1.084     0.9862      1.112        910        640: 100%|██████████| 10/10 [00:03<00:00,  2.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.31it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.787      0.831      0.878      0.608\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     44/100      5.19G      1.112     0.9987       1.12        748        640: 100%|██████████| 10/10 [00:05<00:00,  1.83it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.38it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.775      0.845      0.845      0.583\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     45/100      5.66G      1.043     0.9685      1.116        652        640: 100%|██████████| 10/10 [00:04<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.37it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.811      0.806      0.868      0.608\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     46/100       5.5G      1.049     0.9598      1.113        720        640: 100%|██████████| 10/10 [00:04<00:00,  2.30it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.54it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.774      0.817      0.859       0.61\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     47/100       5.2G      1.048     0.9613      1.104        740        640: 100%|██████████| 10/10 [00:05<00:00,  1.95it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.27it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.823      0.848      0.864      0.592\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     48/100      5.57G      1.033     0.9496      1.097        744        640: 100%|██████████| 10/10 [00:04<00:00,  2.42it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.33it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.855      0.862      0.871      0.626\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     49/100      5.56G      1.042       0.95      1.105        696        640: 100%|██████████| 10/10 [00:06<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.59it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.842      0.789      0.874      0.643\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     50/100      5.59G      1.037     0.9297      1.096        713        640: 100%|██████████| 10/10 [00:04<00:00,  2.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.19it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.818      0.882       0.88      0.625\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     51/100      5.71G      1.031     0.9234      1.095        744        640: 100%|██████████| 10/10 [00:04<00:00,  2.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.50it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.834       0.89      0.874      0.629\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     52/100      5.66G       1.03     0.9192      1.095        801        640: 100%|██████████| 10/10 [00:04<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.30it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.804      0.875      0.859      0.613\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     53/100      5.91G      1.015     0.9281      1.096        662        640: 100%|██████████| 10/10 [00:04<00:00,  2.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.23it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.807      0.841      0.873        0.6\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     54/100      5.53G      1.034     0.9187      1.084        711        640: 100%|██████████| 10/10 [00:06<00:00,  1.59it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.47it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.801      0.893      0.883       0.63\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     55/100      5.32G      1.025     0.9168      1.101        605        640: 100%|██████████| 10/10 [00:04<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.98it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.833      0.879      0.886      0.611\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     56/100      5.54G     0.9939     0.9043      1.082        813        640: 100%|██████████| 10/10 [00:05<00:00,  1.98it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.23it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.82      0.844      0.889      0.652\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     57/100      5.35G      1.019     0.9029      1.088        826        640: 100%|██████████| 10/10 [00:04<00:00,  2.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.14it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.808      0.878      0.897      0.652\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     58/100      5.22G     0.9765     0.8814      1.079        781        640: 100%|██████████| 10/10 [00:03<00:00,  2.56it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.49it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.84       0.86      0.902      0.662\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     59/100      5.47G      1.004     0.8912      1.084        831        640: 100%|██████████| 10/10 [00:05<00:00,  1.68it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.05it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.84      0.856        0.9      0.677\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     60/100       5.3G     0.9895     0.8759      1.076        703        640: 100%|██████████| 10/10 [00:04<00:00,  2.43it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.17it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.815      0.868      0.897      0.677\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     61/100      5.91G     0.9836     0.8902      1.075        745        640: 100%|██████████| 10/10 [00:05<00:00,  1.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.33it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.84       0.88      0.884      0.668\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     62/100      5.72G     0.9771     0.8651      1.067        847        640: 100%|██████████| 10/10 [00:04<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.11it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.817      0.888      0.891      0.674\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     63/100      6.15G     0.9524     0.8531      1.061        736        640: 100%|██████████| 10/10 [00:04<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.64it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.85      0.833      0.899       0.67\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     64/100      6.13G     0.9644     0.8583      1.069        734        640: 100%|██████████| 10/10 [00:05<00:00,  1.73it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.06it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.852      0.873      0.909      0.673\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     65/100      5.23G     0.9717     0.8703      1.071        781        640: 100%|██████████| 10/10 [00:04<00:00,  2.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.98it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.835      0.874      0.897      0.663\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     66/100      5.59G     0.9752      0.852      1.061        705        640: 100%|██████████| 10/10 [00:05<00:00,  1.69it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.21it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.857      0.854       0.89       0.67\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     67/100      5.38G     0.9509      0.847      1.052        738        640: 100%|██████████| 10/10 [00:04<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.14it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.876      0.829       0.88      0.641\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     68/100      5.67G     0.9554     0.8483      1.067        811        640: 100%|██████████| 10/10 [00:04<00:00,  2.22it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.56it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.856      0.822      0.886      0.668\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     69/100       5.6G      0.953     0.8305      1.066        717        640: 100%|██████████| 10/10 [00:05<00:00,  1.91it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.15it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.862      0.797      0.871      0.658\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     70/100      5.41G     0.9284     0.8178      1.042        768        640: 100%|██████████| 10/10 [00:04<00:00,  2.35it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.04it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.832      0.842      0.873      0.656\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     71/100      5.52G     0.9468     0.8366      1.061        692        640: 100%|██████████| 10/10 [00:06<00:00,  1.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.59it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.833      0.877      0.888      0.658\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     72/100      5.88G     0.9304     0.8276      1.049        700        640: 100%|██████████| 10/10 [00:04<00:00,  2.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.75it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.856       0.85      0.885      0.674\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     73/100      5.48G     0.9216      0.803      1.047        723        640: 100%|██████████| 10/10 [00:04<00:00,  2.06it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.82it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.867      0.843      0.896      0.671\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     74/100      5.26G     0.9179     0.8071      1.043        836        640: 100%|██████████| 10/10 [00:04<00:00,  2.03it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.66it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.862       0.85      0.887      0.682\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     75/100      5.35G     0.9404     0.8122      1.048        699        640: 100%|██████████| 10/10 [00:04<00:00,  2.46it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.46it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.87      0.848        0.9      0.688\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     76/100      5.64G     0.9083     0.8024      1.041        804        640: 100%|██████████| 10/10 [00:06<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.47it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.862       0.86      0.908      0.689\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     77/100      5.81G     0.9212     0.8124      1.044        841        640: 100%|██████████| 10/10 [00:04<00:00,  2.40it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.11it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.86      0.867      0.906      0.691\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     78/100      5.24G     0.9119     0.8104      1.044        768        640: 100%|██████████| 10/10 [00:05<00:00,  1.98it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.863      0.852      0.909      0.693\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     79/100      5.43G     0.9169     0.8065      1.041        739        640: 100%|██████████| 10/10 [00:04<00:00,  2.09it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.10it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.853      0.854      0.904       0.69\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     80/100      5.55G     0.9072     0.7933      1.038        811        640: 100%|██████████| 10/10 [00:04<00:00,  2.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.15it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.859      0.847      0.897      0.674\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     81/100      5.38G     0.8941     0.7793      1.033        708        640: 100%|██████████| 10/10 [00:06<00:00,  1.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.91it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.821      0.901      0.905      0.692\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     82/100      5.57G     0.8916      0.771      1.028        665        640: 100%|██████████| 10/10 [00:04<00:00,  2.45it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.29it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.83      0.896      0.902      0.687\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     83/100      5.26G     0.8614     0.7697      1.025        594        640: 100%|██████████| 10/10 [00:05<00:00,  1.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.40it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.845      0.858        0.9      0.687\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     84/100      5.22G     0.8753     0.7635       1.03        752        640: 100%|██████████| 10/10 [00:04<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  3.47it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.822      0.858      0.898      0.694\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     85/100      5.22G     0.8572     0.7535      1.029        623        640: 100%|██████████| 10/10 [00:04<00:00,  2.39it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.53it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.793       0.86      0.879      0.692\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     86/100      6.08G     0.8897     0.7558      1.021        815        640: 100%|██████████| 10/10 [00:06<00:00,  1.62it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.98it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.808      0.809      0.855      0.673\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     87/100      5.57G     0.8805     0.7604      1.012        779        640: 100%|██████████| 10/10 [00:04<00:00,  2.37it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.57it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.787      0.834      0.865      0.692\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     88/100       5.5G     0.8734     0.7536      1.027        579        640: 100%|██████████| 10/10 [00:06<00:00,  1.58it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.28it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.782      0.933        0.9      0.692\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     89/100      5.73G     0.8772     0.7527      1.023        832        640: 100%|██████████| 10/10 [00:04<00:00,  2.33it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.94it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.839      0.849      0.893       0.71\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     90/100      5.22G     0.8754     0.7515      1.022        731        640: 100%|██████████| 10/10 [00:04<00:00,  2.05it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.37it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.864      0.853        0.9      0.713\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "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, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     91/100      4.94G     0.8209      0.756      1.021        462        640: 100%|██████████| 10/10 [00:07<00:00,  1.34it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.43it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.855      0.879      0.909      0.703\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     92/100      4.74G     0.8407     0.7173      1.014        517        640: 100%|██████████| 10/10 [00:05<00:00,  1.88it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.33it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.818      0.893      0.895      0.689\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     93/100      4.79G     0.8067     0.6895      1.009        518        640: 100%|██████████| 10/10 [00:03<00:00,  2.52it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.04it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.834      0.894      0.891       0.68\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     94/100      4.78G     0.8064     0.6786      1.004        505        640: 100%|██████████| 10/10 [00:03<00:00,  2.54it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.34it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.835      0.894      0.893      0.663\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     95/100      4.86G     0.7938     0.6741      1.002        469        640: 100%|██████████| 10/10 [00:06<00:00,  1.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.80it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204       0.82      0.901      0.898      0.679\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     96/100       4.7G     0.7843     0.6619          1        496        640: 100%|██████████| 10/10 [00:04<00:00,  2.44it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.25it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.837      0.913      0.912      0.696\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     97/100      4.65G     0.7895     0.6827     0.9991        478        640: 100%|██████████| 10/10 [00:04<00:00,  2.23it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.43it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.841      0.911      0.913      0.704\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     98/100      4.83G     0.7661      0.652       0.99        519        640: 100%|██████████| 10/10 [00:04<00:00,  2.09it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.69it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.881      0.871      0.914      0.698\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "     99/100      4.75G     0.7529     0.6507     0.9907        469        640: 100%|██████████| 10/10 [00:04<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.27it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.883      0.864      0.911      0.702\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "    100/100      4.76G     0.7752     0.6502     0.9878        484        640: 100%|██████████| 10/10 [00:06<00:00,  1.56it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  1.39it/s]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.871      0.861      0.903      0.704\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "100 epochs completed in 0.202 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 5.5MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 5.5MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics 8.3.55 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLO11n summary (fused): 238 layers, 2,582,737 parameters, 0 gradients, 6.3 GFLOPs\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:00<00:00,  2.12it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                   all         30        204      0.845      0.864      0.894      0.704\n",
            "                person         10         76      0.839      0.974      0.957       0.85\n",
            "      anomalous_action         28         70      0.891      0.843      0.913      0.681\n",
            "         normal_action         20         58      0.806      0.776      0.812       0.58\n",
            "Speed: 0.3ms preprocess, 10.6ms inference, 0.0ms loss, 0.8ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "results = model.train(\n",
        "    data='/content/exam-cheating-2/data.yaml',\n",
        "    epochs=100,\n",
        "    imgsz=640,\n",
        "    batch=32,\n",
        "    lr0=0.001,\n",
        "    optimizer='AdamW',\n",
        "    weight_decay=0.001,\n",
        "    augment=True,\n",
        "    val=True,\n",
        "    patience=20\n",
        "  )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g6djVIPX1sMZ"
      },
      "source": [
        "# Model Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "-XoLR2L41wm9",
        "outputId": "fdeba244-0992-47c1-ee24-993be0c45819"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 6,
          "metadata": {
            "image/png": {
              "width": 1000
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from IPython.display import display, Image\n",
        "\n",
        "Image(filename=f'/content/runs/detect/train/confusion_matrix.png', width=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "up7JROOn2YmR",
        "outputId": "59808b41-2292-414c-8638-61d50202a8b3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 7,
          "metadata": {
            "image/png": {
              "width": 1000
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Image(filename=f'/content/runs/detect/train/results.png', width=1000)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "include_colab_link": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}