{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjpPg4mGKc1v",
        "outputId": "22769f48-3981-4bda-a2e0-62fcc32a4730"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "HOME = os.getcwd()\n",
        "print(HOME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tdSMcABDNKW-",
        "outputId": "98cdb6c9-fdfa-441c-8849-2f8dea0fa833"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "Setup complete ✅ (2 CPUs, 12.7 GB RAM, 26.3/78.2 GB disk)\n"
          ]
        }
      ],
      "source": [
        "# Pip install method (recommended)\n",
        "\n",
        "!pip install ultralytics==8.0.196\n",
        "\n",
        "from IPython import display\n",
        "display.clear_output()\n",
        "\n",
        "import ultralytics\n",
        "ultralytics.checks()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "iVvaIYEEPOty"
      },
      "outputs": [],
      "source": [
        "# Git clone method (for development)\n",
        "\n",
        "# %cd {HOME}\n",
        "# !git clone github.com/ultralytics/ultralytics\n",
        "# %cd {HOME}/ultralytics\n",
        "# !pip install -e .\n",
        "\n",
        "# from IPython import display\n",
        "# display.clear_output()\n",
        "\n",
        "# import ultralytics\n",
        "# ultralytics.checks()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "VOEYrlBoP9-E"
      },
      "outputs": [],
      "source": [
        "from ultralytics import YOLO\n",
        "\n",
        "from IPython.display import display, Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2YkphuiaE7_",
        "outputId": "7faaed7c-2cce-423d-f098-ae4a667bd0cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n",
            "New https://pypi.org/project/ultralytics/8.1.2 available 😃 Update with 'pip install -U ultralytics'\n",
            "Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "\u001b[34m\u001b[1mengine/trainer: \u001b[0mtask=detect, mode=train, model=yolov8s.pt, data=/content/datasets/noti,-pop-2/data.yaml, epochs=25, patience=50, batch=16, imgsz=800, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=None, 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, 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, stream_buffer=False, 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, tracker=botsort.yaml, save_dir=runs/detect/train2\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
            "100% 755k/755k [00:00<00:00, 22.9MB/s]\n",
            "2024-01-18 16:00:22.089883: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-01-18 16:00:22.089941: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-01-18 16:00:22.091831: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "Overriding model.yaml nc=80 with nc=2\n",
            "\n",
            "                   from  n    params  module                                       arguments                     \n",
            "  0                  -1  1       928  ultralytics.nn.modules.conv.Conv             [3, 32, 3, 2]                 \n",
            "  1                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                \n",
            "  2                  -1  1     29056  ultralytics.nn.modules.block.C2f             [64, 64, 1, True]             \n",
            "  3                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               \n",
            "  4                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           \n",
            "  5                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              \n",
            "  6                  -1  2    788480  ultralytics.nn.modules.block.C2f             [256, 256, 2, True]           \n",
            "  7                  -1  1   1180672  ultralytics.nn.modules.conv.Conv             [256, 512, 3, 2]              \n",
            "  8                  -1  1   1838080  ultralytics.nn.modules.block.C2f             [512, 512, 1, 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  1    591360  ultralytics.nn.modules.block.C2f             [768, 256, 1]                 \n",
            " 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          \n",
            " 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 15                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 \n",
            " 16                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              \n",
            " 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 18                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 \n",
            " 19                  -1  1    590336  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 2]              \n",
            " 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           \n",
            " 21                  -1  1   1969152  ultralytics.nn.modules.block.C2f             [768, 512, 1]                 \n",
            " 22        [15, 18, 21]  1   2116822  ultralytics.nn.modules.head.Detect           [2, [128, 256, 512]]          \n",
            "Model summary: 225 layers, 11136374 parameters, 11136358 gradients, 28.6 GFLOPs\n",
            "\n",
            "Transferred 349/355 items from pretrained weights\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/detect/train2', view at http://localhost:6006/\n",
            "Freezing layer 'model.22.dfl.conv.weight'\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mrunning Automatic Mixed Precision (AMP) checks with YOLOv8n...\n",
            "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt to 'yolov8n.pt'...\n",
            "100% 6.23M/6.23M [00:00<00:00, 131MB/s]\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/datasets/noti,-pop-2/train/labels... 99 images, 0 backgrounds, 0 corrupt: 100% 99/99 [00:00<00:00, 1584.89it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/datasets/noti,-pop-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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/noti,-pop-2/valid/labels... 0 images, 1 backgrounds, 0 corrupt: 100% 1/1 [00:00<00:00, 564.28it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mWARNING ⚠️ No labels found in /content/datasets/noti,-pop-2/valid/labels.cache. See https://docs.ultralytics.com/datasets/detect for dataset formatting guidance.\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/noti,-pop-2/valid/labels.cache\n",
            "WARNING ⚠️ No labels found in /content/datasets/noti,-pop-2/valid/labels.cache, training may not work correctly. See https://docs.ultralytics.com/datasets/detect for dataset formatting guidance.\n",
            "Plotting labels to runs/detect/train2/labels.jpg... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... \n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m AdamW(lr=0.001667, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)\n",
            "Image sizes 800 train, 800 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/detect/train2\u001b[0m\n",
            "Starting training for 25 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       1/25      6.49G      1.725      6.284      1.878          5        800: 100% 7/7 [00:07<00:00,  1.12s/it]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  4.21it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       2/25       6.7G      1.086      3.888      1.449          6        800: 100% 7/7 [00:02<00:00,  2.38it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  7.04it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       3/25      6.78G     0.6288      1.834      1.135          9        800: 100% 7/7 [00:02<00:00,  2.66it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 14.89it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       4/25      6.77G     0.5808      1.567      1.087         10        800: 100% 7/7 [00:02<00:00,  2.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 12.34it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       5/25      6.81G     0.6111       1.42      1.086          7        800: 100% 7/7 [00:02<00:00,  2.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 23.66it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       6/25      6.77G     0.5933      1.403      1.113          7        800: 100% 7/7 [00:02<00:00,  2.75it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 16.21it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       7/25       6.8G     0.6345      1.435      1.129          5        800: 100% 7/7 [00:02<00:00,  2.47it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 11.64it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       8/25      6.78G     0.6892      1.233      1.253          7        800: 100% 7/7 [00:02<00:00,  2.69it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 14.47it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "       9/25      6.81G     0.6554      1.138      1.135          8        800: 100% 7/7 [00:02<00:00,  2.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 27.78it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      10/25      6.77G     0.6832      1.138      1.166          8        800: 100% 7/7 [00:02<00:00,  2.76it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 30.12it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      11/25      6.78G     0.6651      1.047      1.148          6        800: 100% 7/7 [00:02<00:00,  2.53it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  8.26it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      12/25      6.81G      0.586      1.065      1.103          8        800: 100% 7/7 [00:02<00:00,  2.77it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 12.39it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      13/25      6.82G     0.6061      1.056      1.068         10        800: 100% 7/7 [00:02<00:00,  2.84it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 12.55it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      14/25      6.81G     0.6334      1.059      1.086         11        800: 100% 7/7 [00:02<00:00,  2.41it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 16.73it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      15/25      6.79G     0.6237       1.05      1.094          5        800: 100% 7/7 [00:02<00:00,  2.86it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 17.39it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\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), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      16/25      6.79G     0.6507      1.278      1.225          3        800: 100% 7/7 [00:04<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 12.16it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      17/25      6.78G     0.6151      1.228      1.162          3        800: 100% 7/7 [00:02<00:00,  2.74it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 30.73it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      18/25      6.81G     0.6377     0.9792       1.16          3        800: 100% 7/7 [00:02<00:00,  2.89it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 17.83it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      19/25      6.78G     0.5092     0.7725      1.096          3        800: 100% 7/7 [00:02<00:00,  2.78it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 19.17it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      20/25      6.78G     0.6106     0.8613      1.175          3        800: 100% 7/7 [00:02<00:00,  2.50it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 10.97it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      21/25      6.79G     0.5204     0.7634      1.052          8        800: 100% 7/7 [00:02<00:00,  2.89it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 30.26it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      22/25      6.81G     0.5995     0.7854      1.128          3        800: 100% 7/7 [00:02<00:00,  2.90it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 14.64it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      23/25      6.79G     0.5311     0.7088       1.14          3        800: 100% 7/7 [00:02<00:00,  2.64it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 12.45it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      24/25      6.78G     0.4875     0.7241     0.9857          3        800: 100% 7/7 [00:02<00:00,  2.80it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 29.86it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
            "      25/25      6.78G     0.5477     0.6431      1.056          3        800: 100% 7/7 [00:02<00:00,  2.84it/s]\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 28.19it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "\n",
            "25 epochs completed in 0.033 hours.\n",
            "Optimizer stripped from runs/detect/train2/weights/last.pt, 22.5MB\n",
            "Optimizer stripped from runs/detect/train2/weights/best.pt, 22.5MB\n",
            "\n",
            "Validating runs/detect/train2/weights/best.pt...\n",
            "Ultralytics YOLOv8.0.196 🚀 Python-3.10.12 torch-2.1.0+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
            "Model summary (fused): 168 layers, 11126358 parameters, 0 gradients, 28.4 GFLOPs\n",
            "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00, 55.08it/s]\n",
            "                   all          1          0          0          0          0          0\n",
            "WARNING ⚠️ no labels found in detect set, can not compute metrics without labels\n",
            "Speed: 0.2ms preprocess, 10.0ms inference, 0.0ms loss, 1.3ms postprocess per image\n",
            "Results saved to \u001b[1mruns/detect/train2\u001b[0m\n",
            "💡 Learn more at https://docs.ultralytics.com/modes/train\n"
          ]
        }
      ],
      "source": [
        "%cd {HOME}\n",
        "\n",
        "!yolo task=detect mode=train model=yolov8s.pt data={dataset.location}/data.yaml epochs=125 imgsz=800 plots=True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1MScstfHhArr",
        "outputId": "4357c7e3-4cf9-4af1-c2f5-a128a1b5e77b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "args.yaml  weights\n"
          ]
        }
      ],
      "source": [
        "!ls {HOME}/runs/detect/train/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "A-urTWUkhRmn",
        "outputId": "1b1373f7-bcfc-43ec-c78b-fad82046d0f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        },
        {
          "data": {
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              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "image/png": {
              "width": 600
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%cd {HOME}\n",
        "Image(filename=f'{HOME}/runs/detect/train2/results.png', width=600)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "HI4nADCCj3F5",
        "outputId": "05e36ed3-5ffb-4835-b720-299e7eca9ed3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n"
          ]
        },
        {
          "data": {
            "image/jpeg": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "execution_count": 14,
          "metadata": {
            "image/jpeg": {
              "width": 600
            }
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "%cd {HOME}\n",
        "Image(filename=f'{HOME}/runs/detect/train2/val_batch0_pred.jpg', width=600)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}