yolo8detectcustom2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxndXyGuYJWy",
        "outputId": "98ada962-966d-4023-83ae-3096f6094301"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting ultralytics\n",
            "  Downloading ultralytics-8.0.93-py3-none-any.whl (565 kB)\n",
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            "Installing collected packages: sentry-sdk, thop, ultralytics\n",
            "Successfully installed sentry-sdk-1.22.1 thop-0.1.1.post2209072238 ultralytics-8.0.93\n"
          ]
        }
      ],
      "source": [
        "%pip install ultralytics\n",
        "import ultralytics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zlv7Ow9oYrxg",
        "outputId": "d0a99aa5-603f-4d28-b6b9-809421f6f889"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting roboflow\n",
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            "Installing collected packages: wget, python-dotenv, pyparsing, idna, cycler, requests-toolbelt, roboflow\n",
            "  Attempting uninstall: pyparsing\n",
            "    Found existing installation: pyparsing 3.0.9\n",
            "    Uninstalling pyparsing-3.0.9:\n",
            "      Successfully uninstalled pyparsing-3.0.9\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.4\n",
            "    Uninstalling idna-3.4:\n",
            "      Successfully uninstalled idna-3.4\n",
            "  Attempting uninstall: cycler\n",
            "    Found existing installation: cycler 0.11.0\n",
            "    Uninstalling cycler-0.11.0:\n",
            "      Successfully uninstalled cycler-0.11.0\n",
            "Successfully installed cycler-0.10.0 idna-2.10 pyparsing-2.4.7 python-dotenv-1.0.0 requests-toolbelt-1.0.0 roboflow-1.0.8 wget-3.2\n"
          ]
        },
        {
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "cycler",
                  "idna",
                  "pyparsing"
                ]
              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Dependency ultralytics<=8.0.20 is required but found version=8.0.93, to fix: `pip install ultralytics<=8.0.20`\n",
            "Downloading Dataset Version Zip in Gender-1 to yolov8: 100% [57027960 / 57027960] bytes\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Extracting Dataset Version Zip to Gender-1 in yolov8:: 100%|██████████| 6142/6142 [00:01<00:00, 3616.90it/s]\n"
          ]
        }
      ],
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"\")\n",
        "project = rf.workspace(\"madz-csfto\").project(\"gender-ofwae\")\n",
        "dataset = project.version(1).download(\"yolov8\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n2XL_nBYZG3y",
        "outputId": "5c531a50-2dcf-41d6-9110-ec7beafebe84"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.SPPF                  [256, 256, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 12                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                  \n",
            " 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 \n",
            " 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
            " 22        [15, 18, 21]  1    897664  ultralytics.nn.modules.Detect                [80, [64, 128, 256]]          \n",
            "YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs\n",
            "\n",
            "Ultralytics YOLOv8.0.93 🚀 Python-3.10.11 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1myolo/engine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8n.yaml, data=/content/Gender-1/data.yaml, epochs=20, patience=50, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, exist_ok=False, pretrained=False, optimizer=SGD, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=0, resume=False, amp=True, 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, show=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, vid_stride=1, line_width=None, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, boxes=True, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=None, workspace=4, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0, cfg=None, v5loader=False, tracker=botsort.yaml, save_dir=runs/detect/train2\n",
            "Overriding model.yaml nc=80 with nc=2\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       464  ultralytics.nn.modules.Conv                  [3, 16, 3, 2]                 \n",
            "  1                  -1  1      4672  ultralytics.nn.modules.Conv                  [16, 32, 3, 2]                \n",
            "  2                  -1  1      7360  ultralytics.nn.modules.C2f                   [32, 32, 1, True]             \n",
            "  3                  -1  1     18560  ultralytics.nn.modules.Conv                  [32, 64, 3, 2]                \n",
            "  4                  -1  2     49664  ultralytics.nn.modules.C2f                   [64, 64, 2, True]             \n",
            "  5                  -1  1     73984  ultralytics.nn.modules.Conv                  [64, 128, 3, 2]               \n",
            "  6                  -1  2    197632  ultralytics.nn.modules.C2f                   [128, 128, 2, True]           \n",
            "  7                  -1  1    295424  ultralytics.nn.modules.Conv                  [128, 256, 3, 2]              \n",
            "  8                  -1  1    460288  ultralytics.nn.modules.C2f                   [256, 256, 1, True]           \n",
            "  9                  -1  1    164608  ultralytics.nn.modules.SPPF                  [256, 256, 5]                 \n",
            " 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 12                  -1  1    148224  ultralytics.nn.modules.C2f                   [384, 128, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 15                  -1  1     37248  ultralytics.nn.modules.C2f                   [192, 64, 1]                  \n",
            " 16                  -1  1     36992  ultralytics.nn.modules.Conv                  [64, 64, 3, 2]                \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 18                  -1  1    123648  ultralytics.nn.modules.C2f                   [192, 128, 1]                 \n",
            " 19                  -1  1    147712  ultralytics.nn.modules.Conv                  [128, 128, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           \n",
            " 21                  -1  1    493056  ultralytics.nn.modules.C2f                   [384, 256, 1]                 \n",
            " 22        [15, 18, 21]  1    751702  ultralytics.nn.modules.Detect                [2, [64, 128, 256]]           \n",
            "YOLOv8n summary: 225 layers, 3011238 parameters, 3011222 gradients, 8.2 GFLOPs\n",
            "\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train2', view at http://localhost:6006/\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/Gender-1/train/labels.cache... 2673 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2673/2673 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Gender-1/valid/labels.cache... 248 images, 0 backgrounds, 0 corrupt: 100%|██████████| 248/248 [00:00<?, ?it/s]\n",
            "Plotting labels to runs/detect/train2/labels.jpg... \n",
            "Image sizes 640 train, 640 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train2\u001b[0m\n",
            "Starting training for 20 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       1/20      2.42G      3.006      3.765      3.987          4        640: 100%|██████████| 168/168 [00:58<00:00,  2.90it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  2.94it/s]\n",
            "                   all        248        250     0.0032      0.839     0.0309     0.0097\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       2/20      2.31G       2.84      3.189      3.547          1        640: 100%|██████████| 168/168 [00:56<00:00,  2.96it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:03<00:00,  2.54it/s]\n",
            "                   all        248        250    0.00957      0.666     0.0154    0.00445\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       3/20      2.29G      2.123      2.419      2.814          4        640: 100%|██████████| 168/168 [00:55<00:00,  3.01it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.09it/s]\n",
            "                   all        248        250      0.389      0.675      0.458      0.217\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       4/20      2.29G      1.603      1.932      2.255          4        640: 100%|██████████| 168/168 [00:56<00:00,  2.99it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:03<00:00,  2.05it/s]\n",
            "                   all        248        250      0.533      0.791      0.637      0.401\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       5/20      2.29G      1.339      1.602      1.957          1        640: 100%|██████████| 168/168 [00:55<00:00,  3.03it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  2.93it/s]\n",
            "                   all        248        250      0.594       0.84      0.779      0.558\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       6/20      2.29G      1.191      1.353      1.795          1        640: 100%|██████████| 168/168 [00:54<00:00,  3.09it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.01it/s]\n",
            "                   all        248        250      0.656      0.832       0.85      0.611\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       7/20      2.29G      1.088      1.189      1.685          1        640: 100%|██████████| 168/168 [00:55<00:00,  3.02it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:03<00:00,  2.23it/s]\n",
            "                   all        248        250      0.793      0.885      0.916      0.699\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       8/20      2.29G      1.026      1.059      1.629          1        640: 100%|██████████| 168/168 [00:55<00:00,  3.05it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.06it/s]\n",
            "                   all        248        250      0.762       0.91      0.932      0.724\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       9/20      2.29G     0.9843     0.9916      1.579          4        640: 100%|██████████| 168/168 [00:54<00:00,  3.06it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.07it/s]\n",
            "                   all        248        250      0.782      0.908      0.943       0.72\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      10/20      2.29G     0.9366     0.9094      1.548          4        640: 100%|██████████| 168/168 [00:54<00:00,  3.06it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:03<00:00,  2.21it/s]\n",
            "                   all        248        250      0.922      0.878      0.958      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      11/20      2.29G     0.9239     0.8695      1.521          3        640: 100%|██████████| 168/168 [00:55<00:00,  3.03it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.08it/s]\n",
            "                   all        248        250      0.888      0.904      0.951      0.758\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      12/20      2.29G     0.8919     0.8146      1.503          2        640: 100%|██████████| 168/168 [00:55<00:00,  3.05it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:03<00:00,  2.65it/s]\n",
            "                   all        248        250      0.899      0.899       0.95      0.754\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      13/20      2.29G     0.8773     0.8017      1.483          1        640: 100%|██████████| 168/168 [00:55<00:00,  3.00it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.10it/s]\n",
            "                   all        248        250      0.931      0.925      0.966      0.772\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      14/20      2.29G     0.8446     0.7644      1.468          4        640: 100%|██████████| 168/168 [00:54<00:00,  3.07it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.10it/s]\n",
            "                   all        248        250      0.893      0.911      0.962      0.775\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      15/20      2.29G     0.8474     0.7546       1.46          4        640: 100%|██████████| 168/168 [00:54<00:00,  3.07it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  2.75it/s]\n",
            "                   all        248        250      0.961      0.921      0.967      0.775\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      16/20      2.29G     0.8358     0.7308      1.444          2        640: 100%|██████████| 168/168 [00:55<00:00,  3.04it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  2.90it/s]\n",
            "                   all        248        250      0.938      0.942      0.975      0.792\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      17/20      2.29G     0.8092     0.7112       1.43          2        640: 100%|██████████| 168/168 [00:55<00:00,  3.05it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  3.07it/s]\n",
            "                   all        248        250      0.946      0.946      0.976      0.787\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      18/20      2.29G      0.778     0.6712       1.41          2        640: 100%|██████████| 168/168 [00:55<00:00,  3.03it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:03<00:00,  2.60it/s]\n",
            "                   all        248        250      0.952      0.963       0.98      0.796\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      19/20      2.29G     0.7942     0.6497      1.419          2        640: 100%|██████████| 168/168 [00:55<00:00,  3.04it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:02<00:00,  2.96it/s]\n",
            "                   all        248        250      0.926      0.955      0.977      0.798\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      20/20      2.29G     0.7742      0.646      1.401          3        640: 100%|██████████| 168/168 [00:55<00:00,  3.03it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:06<00:00,  1.27it/s]\n",
            "                   all        248        250      0.904      0.964      0.981      0.811\n",
            "\n",
            "20 epochs completed in 0.329 hours.\n",
            "Optimizer stripped from runs/detect/train2/weights/last.pt, 6.2MB\n",
            "Optimizer stripped from runs/detect/train2/weights/best.pt, 6.2MB\n",
            "\n",
            "Validating runs/detect/train2/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.93 🚀 Python-3.10.11 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8n summary (fused): 168 layers, 3006038 parameters, 0 gradients, 8.1 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 8/8 [00:05<00:00,  1.55it/s]\n",
            "                   all        248        250      0.908      0.967      0.982      0.811\n",
            "                Female        248        153      0.847       0.98      0.987      0.805\n",
            "                  Male        248         97      0.969      0.954      0.977      0.818\n",
            "Speed: 2.1ms preprocess, 2.4ms inference, 0.0ms loss, 3.0ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train2\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "# Load a model\n",
        "model = YOLO('yolov8n.yaml')  \n",
        "\n",
        "model.train(data='/content/Gender-1/data.yaml', epochs=20, imgsz=640)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7MTTpt8mcMgw",
        "outputId": "1b5b1083-6602-4413-909c-f36903f00106"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Ultralytics YOLOv8.0.93 🚀 Python-3.10.11 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
            "YOLOv8n summary (fused): 168 layers, 3006038 parameters, 0 gradients, 8.1 GFLOPs\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/Gender-1/valid/labels.cache... 248 images, 0 backgrounds, 0 corrupt: 100%|██████████| 248/248 [00:00<?, ?it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 16/16 [00:05<00:00,  2.84it/s]\n",
            "                   all        248        250      0.714      0.708      0.763      0.541\n",
            "                Female        248        153      0.722       0.85      0.813      0.557\n",
            "                  Male        248         97      0.706      0.567      0.713      0.526\n",
            "Speed: 2.5ms preprocess, 5.4ms inference, 0.0ms loss, 2.3ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/val\u001b[0m\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "ultralytics.yolo.utils.metrics.DetMetrics object with attributes:\n",
              "\n",
              "ap_class_index: array([0, 1])\n",
              "box: ultralytics.yolo.utils.metrics.Metric object\n",
              "confusion_matrix: <ultralytics.yolo.utils.metrics.ConfusionMatrix object at 0x7efea2b5a2c0>\n",
              "fitness: 0.5635634965081521\n",
              "keys: ['metrics/precision(B)', 'metrics/recall(B)', 'metrics/mAP50(B)', 'metrics/mAP50-95(B)']\n",
              "maps: array([    0.55708,     0.52571])\n",
              "names: {0: 'Female', 1: 'Male'}\n",
              "plot: True\n",
              "results_dict: {'metrics/precision(B)': 0.71408298782353, 'metrics/recall(B)': 0.7082693447296067, 'metrics/mAP50(B)': 0.7631031626520666, 'metrics/mAP50-95(B)': 0.5413924224921616, 'fitness': 0.5635634965081521}\n",
              "save_dir: PosixPath('runs/detect/val')\n",
              "speed: {'preprocess': 2.4685138656247045, 'inference': 5.350537838474397, 'loss': 0.0012853453236241496, 'postprocess': 2.2909670106826288}"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = YOLO('/content/runs/detect/train/weights/best.pt')  \n",
        "\n",
        "model.val()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}