ppe_dectections.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1OLFCi5gxT60NhhtS2ouKlZRe0axA5W5G",
      "authorship_tag": "ABX9TyOXFTdalQyyge8oqJ89qJuE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iamkanhaiyakumar/PPE_detection_Kit/blob/main/ppe_dectections.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qHwG0jBMng_",
        "outputId": "3ecaf251-bfd5-4979-c21a-78f16945a453"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sun Nov 24 08:32:37 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   45C    P8              10W /  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": "code",
      "source": [
        "!pip install ultralytics"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QjX3bXHlNc4J",
        "outputId": "d9f9315f-6974-422c-fc43-e04328c4cd01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.3.36-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) (4.66.6)\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-2.0.12-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) (4.55.0)\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) (3.10)\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) (2024.8.30)\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) (1.16.0)\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-8.3.36-py3-none-any.whl (887 kB)\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m887.3/887.3 kB\u001b[0m \u001b[31m27.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hDownloading ultralytics_thop-2.0.12-py3-none-any.whl (26 kB)\n",
            "Installing collected packages: ultralytics-thop, ultralytics\n",
            "Successfully installed ultralytics-8.3.36 ultralytics-thop-2.0.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install opencv-python"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1ghvNZ7rNjNK",
        "outputId": "e3613d08-82d9-474f-95c2-1e4634264f11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (4.10.0.84)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from opencv-python) (1.26.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install cvzone"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RxBy6LXRNmiJ",
        "outputId": "7b39da05-7f3c-4b32-e10f-5c4262e466a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting cvzone\n",
            "  Downloading cvzone-1.6.1.tar.gz (25 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.10/dist-packages (from cvzone) (4.10.0.84)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from cvzone) (1.26.4)\n",
            "Building wheels for collected packages: cvzone\n",
            "  Building wheel for cvzone (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for cvzone: filename=cvzone-1.6.1-py3-none-any.whl size=26298 sha256=4b1214603b7588e2de60fe5611d2587a38872ca33b4070f5275894470e5a3fc1\n",
            "  Stored in directory: /root/.cache/pip/wheels/2c/9f/b3/92e945ac4a71bf727a92463f38155cc5a4fa49c5010b38ec4c\n",
            "Successfully built cvzone\n",
            "Installing collected packages: cvzone\n",
            "Successfully installed cvzone-1.6.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from ultralytics import YOLO\n",
        "import cv2\n",
        "import cvzone\n",
        "import math"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PSgj6Rm-NBIg",
        "outputId": "7e7e2218-f2fc-49c6-96d8-5eb816eec267"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect mode=predict model=yolov8l.pt conf=0.25 source='https://ultralytics.com/images/bus.jpg'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PZfW40YtNJw5",
        "outputId": "bfb5c2b5-beb3-4086-d78c-c4847e4ea1ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolov8l.pt to 'yolov8l.pt'...\n",
            "100% 83.7M/83.7M [00:00<00:00, 316MB/s]\n",
            "Ultralytics 8.3.36 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8l summary (fused): 268 layers, 43,668,288 parameters, 0 gradients, 165.2 GFLOPs\n",
            "\n",
            "Downloading https://ultralytics.com/images/bus.jpg to 'bus.jpg'...\n",
            "100% 134k/134k [00:00<00:00, 6.21MB/s]\n",
            "image 1/1 /content/bus.jpg: 640x480 5 persons, 1 bicycle, 1 bus, 137.3ms\n",
            "Speed: 15.2ms preprocess, 137.3ms inference, 684.3ms postprocess per image at shape (1, 3, 640, 480)\n",
            "Results saved to \u001b[1mruns/detect/predict\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/predict\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!yolo task=detect mode=train model=yolov8l.pt data=/content/drive/MyDrive/Datasets/ConstructionSafety/data.yaml epochs=30 imgsz=640"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iiPM-BUCNLFK",
        "outputId": "e0f474dd-db0a-42d0-eddd-8fbf835b40c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ultralytics 8.3.36 🚀 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=yolov8l.pt, data=/content/drive/MyDrive/Datasets/ConstructionSafety/data.yaml, epochs=30, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, 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",
            "100% 755k/755k [00:00<00:00, 17.7MB/s]\n",
            "Overriding model.yaml nc=80 with nc=10\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1      1856  ultralytics.nn.modules.conv.Conv             [3, 64, 3, 2]                 \n",
            "  1                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  2                  -1  3    279808  ultralytics.nn.modules.block.C2f             [128, 128, 3, True]           \n",
            "  3                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  4                  -1  6   2101248  ultralytics.nn.modules.block.C2f             [256, 256, 6, True]           \n",
            "  5                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
            "  6                  -1  6   8396800  ultralytics.nn.modules.block.C2f             [512, 512, 6, True]           \n",
            "  7                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n",
            "  8                  -1  3   4461568  ultralytics.nn.modules.block.C2f             [512, 512, 3, True]           \n",
            "  9                  -1  1    656896  ultralytics.nn.modules.block.SPPF            [512, 512, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 12                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  3   1247744  ultralytics.nn.modules.block.C2f             [768, 256, 3]                 \n",
            " 16                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  3   4592640  ultralytics.nn.modules.block.C2f             [768, 512, 3]                 \n",
            " 19                  -1  1   2360320  ultralytics.nn.modules.conv.Conv             [512, 512, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  3   4723712  ultralytics.nn.modules.block.C2f             [1024, 512, 3]                \n",
            " 22        [15, 18, 21]  1   5590510  ultralytics.nn.modules.head.Detect           [10, [256, 512, 512]]         \n",
            "Model summary: 365 layers, 43,637,550 parameters, 43,637,534 gradients, 165.4 GFLOPs\n",
            "\n",
            "Transferred 589/595 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.22.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11n.pt to 'yolo11n.pt'...\n",
            "100% 5.35M/5.35M [00:00<00:00, 87.8MB/s]\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/drive/MyDrive/Datasets/ConstructionSafety/train/labels.cache... 2605 images, 16 backgrounds, 0 corrupt: 100% 2615/2615 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/Datasets/ConstructionSafety/train/images/004720_jpg.rf.afc486560a4004c7cfd67910af31a29c.jpg: 1 duplicate labels removed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING ⚠️ /content/drive/MyDrive/Datasets/ConstructionSafety/train/images/construction-813-_jpg.rf.b085952261fd98f2e76b8065de149b5f.jpg: 1 duplicate labels removed\n",
            "/usr/local/lib/python3.10/dist-packages/albumentations/__init__.py:24: UserWarning: A new version of Albumentations is available: 1.4.21 (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",
            "  check_for_updates()\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",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/drive/MyDrive/Datasets/ConstructionSafety/valid/labels.cache... 114 images, 10 backgrounds, 0 corrupt: 100% 114/114 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.000714, momentum=0.9) with parameter groups 97 weight(decay=0.0), 104 weight(decay=0.0005), 103 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 30 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       1/30      10.4G      1.249      1.742      1.462        147        640: 100% 164/164 [07:39<00:00,  2.80s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.09it/s]\n",
            "                   all        114        697      0.584      0.408      0.429      0.191\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       2/30      10.3G      1.252      1.523      1.455        157        640: 100% 164/164 [02:20<00:00,  1.16it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.30it/s]\n",
            "                   all        114        697      0.584      0.479      0.466      0.194\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       3/30      10.6G      1.253        1.5      1.451        146        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.52it/s]\n",
            "                   all        114        697      0.631      0.455      0.495      0.233\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       4/30      10.3G      1.229      1.485      1.439        215        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.26it/s]\n",
            "                   all        114        697      0.661       0.46      0.499      0.238\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       5/30      10.4G      1.188      1.378      1.406         96        640: 100% 164/164 [02:24<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.54it/s]\n",
            "                   all        114        697      0.741      0.535      0.605      0.272\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       6/30      10.3G      1.145       1.27      1.377        104        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.28it/s]\n",
            "                   all        114        697      0.718      0.544        0.6      0.275\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       7/30      10.2G      1.095      1.183      1.345        130        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.54it/s]\n",
            "                   all        114        697      0.812      0.546      0.643      0.299\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       8/30      10.3G      1.072      1.125      1.319        196        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.54it/s]\n",
            "                   all        114        697      0.765      0.566      0.633      0.338\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       9/30      10.3G      1.056      1.074      1.305        161        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.49it/s]\n",
            "                   all        114        697      0.773      0.636      0.695      0.356\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      10/30      9.99G      1.038      1.015      1.288        147        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.31it/s]\n",
            "                   all        114        697       0.81      0.637      0.714       0.36\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      11/30      10.6G       1.01     0.9785      1.272        152        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.35it/s]\n",
            "                   all        114        697      0.816      0.647      0.726      0.372\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      12/30      10.3G      0.981     0.9263      1.252        132        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.54it/s]\n",
            "                   all        114        697      0.853      0.621      0.705      0.377\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      13/30      10.2G     0.9716     0.8964      1.241        106        640: 100% 164/164 [02:25<00:00,  1.12it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.48it/s]\n",
            "                   all        114        697      0.874      0.657      0.733      0.388\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      14/30      10.2G     0.9361      0.847      1.218        210        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.39it/s]\n",
            "                   all        114        697      0.903      0.673      0.764      0.416\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      15/30      10.4G     0.9217     0.8227      1.211        109        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:03<00:00,  1.29it/s]\n",
            "                   all        114        697      0.852      0.689      0.753      0.417\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      16/30      10.4G     0.9072     0.7957      1.203        225        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.55it/s]\n",
            "                   all        114        697      0.872      0.675      0.761      0.447\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      17/30      10.2G     0.8871      0.769      1.187        122        640: 100% 164/164 [02:25<00:00,  1.12it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.38it/s]\n",
            "                   all        114        697      0.871      0.708      0.779      0.458\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      18/30      10.2G     0.8599      0.732       1.17        253        640: 100% 164/164 [02:25<00:00,  1.12it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.57it/s]\n",
            "                   all        114        697      0.902      0.706      0.788      0.453\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      19/30      10.2G     0.8464     0.7084      1.156        164        640: 100% 164/164 [02:25<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.46it/s]\n",
            "                   all        114        697      0.899      0.715      0.798      0.496\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      20/30      10.1G     0.8236      0.686      1.145        147        640: 100% 164/164 [02:25<00:00,  1.12it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.50it/s]\n",
            "                   all        114        697      0.887      0.744       0.81      0.484\n",
            "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",
            "      21/30      10.3G     0.8472     0.6171       1.16         67        640: 100% 164/164 [02:24<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.53it/s]\n",
            "                   all        114        697      0.906      0.725      0.798      0.495\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      22/30      10.2G     0.8118     0.5761      1.136        162        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.54it/s]\n",
            "                   all        114        697      0.925      0.732       0.82      0.518\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      23/30      10.4G     0.7878     0.5455      1.117        101        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.55it/s]\n",
            "                   all        114        697      0.878      0.771      0.826      0.524\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      24/30      10.4G     0.7647     0.5243      1.104         67        640: 100% 164/164 [02:24<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.43it/s]\n",
            "                   all        114        697      0.897      0.753      0.827       0.54\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      25/30      10.3G     0.7386     0.5024      1.089         60        640: 100% 164/164 [02:24<00:00,  1.13it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.47it/s]\n",
            "                   all        114        697      0.901      0.759       0.83      0.531\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      26/30      10.3G     0.7177     0.4831      1.072         82        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.42it/s]\n",
            "                   all        114        697      0.907      0.759      0.834      0.548\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      27/30      10.3G     0.7002     0.4607      1.064         82        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.54it/s]\n",
            "                   all        114        697      0.904      0.784      0.843      0.564\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      28/30      10.3G     0.6849     0.4466      1.054         78        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.57it/s]\n",
            "                   all        114        697        0.9       0.78      0.843      0.554\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      29/30      10.2G     0.6623     0.4343      1.041         68        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.55it/s]\n",
            "                   all        114        697      0.922      0.773      0.851      0.561\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      30/30      10.3G     0.6461     0.4182      1.027         77        640: 100% 164/164 [02:24<00:00,  1.14it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:02<00:00,  1.43it/s]\n",
            "                   all        114        697      0.908      0.793      0.853      0.572\n",
            "\n",
            "30 epochs completed in 1.378 hours.\n",
            "Optimizer stripped from runs/detect/train/weights/last.pt, 87.7MB\n",
            "Optimizer stripped from runs/detect/train/weights/best.pt, 87.7MB\n",
            "\n",
            "Validating runs/detect/train/weights/best.pt...\n",
            "Ultralytics 8.3.36 🚀 Python-3.10.12 torch-2.5.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "Model summary (fused): 268 layers, 43,614,318 parameters, 0 gradients, 164.9 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 4/4 [00:04<00:00,  1.11s/it]\n",
            "                   all        114        697       0.91      0.789      0.853      0.571\n",
            "               Hardhat         42         79      0.942       0.83      0.914      0.626\n",
            "                  Mask         19         21      0.943      0.905       0.95      0.703\n",
            "            NO-Hardhat         37         69      0.933      0.601      0.784      0.445\n",
            "               NO-Mask         44         74      0.819      0.673      0.702      0.429\n",
            "        NO-Safety Vest         56        106      0.929      0.746      0.836      0.521\n",
            "                Person         84        166      0.931      0.813      0.876      0.606\n",
            "           Safety Cone         13         44      0.895      0.886      0.924      0.551\n",
            "           Safety Vest         28         41          1      0.917      0.931      0.679\n",
            "             machinery         26         55      0.945      0.891      0.938      0.724\n",
            "               vehicle         16         42      0.767      0.626      0.675      0.429\n",
            "Speed: 0.5ms preprocess, 19.9ms inference, 0.0ms loss, 3.8ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/train\n"
          ]
        }
      ]
    }
  ]
}