training.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "G600tes1fYLf",
        "outputId": "6d067aac-ef5e-4688-acd5-51c9d327f326",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip drive/MyDrive/class_dataset.zip\n",
        "!git clone https://github.com/ultralytics/yolov5\n",
        "!pip install -r yolov5/requirements.txt\n",
        "!touch yolov5/data/class_dataset.yaml"
      ],
      "metadata": {
        "id": "oppoMR8ffZNv",
        "outputId": "780db0de-60a3-4ea7-ed9c-bf8cedd7d834",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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    },
    {
      "cell_type": "code",
      "source": [
        "!python yolov5/train.py --img 192 --batch 256 --epochs 50 --data yolov5/data/class_dataset.yaml --weights yolov5s.pt --cache\n",
        "\n",
        "import shutil\n",
        "shutil.make_archive('experiment', format='zip', root_dir='yolov5/runs/train/exp')\n",
        "\n",
        "from google.colab import files\n",
        "files.download('experiment.zip')"
      ],
      "metadata": {
        "id": "leUXjMJ1f37Z",
        "outputId": "5779531e-1402-443b-8102-90d51e650782",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=yolov5/data/class_dataset.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=50, batch_size=256, imgsz=192, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v7.0-53-g65071da Python-3.8.16 torch-1.13.0+cu116 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=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=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, 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\n",
            "\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n",
            "\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/\n",
            "Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n",
            "100% 755k/755k [00:00<00:00, 164MB/s]\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt to yolov5s.pt...\n",
            "100% 14.1M/14.1M [00:01<00:00, 12.9MB/s]\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=78\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  2    115712  models.common.C3                        [128, 128, 2]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 \n",
            "  9                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1    223851  models.yolo.Detect                      [78, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model summary: 214 layers, 7229995 parameters, 7229995 gradients, 16.6 GFLOPs\n",
            "\n",
            "Transferred 343/349 items from yolov5s.pt\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), 60 weight(decay=0.002), 60 bias\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[1mtrain: \u001b[0mScanning /content/class_dataset/labels/train... 29668 images, 3 backgrounds, 0 corrupt: 100% 29671/29671 [01:03<00:00, 467.11it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: /content/class_dataset/labels/train.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (2.2GB ram): 100% 29671/29671 [03:35<00:00, 137.79it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/class_dataset/labels/val... 15972 images, 5 backgrounds, 0 corrupt: 100% 15977/15977 [00:39<00:00, 403.38it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/class_dataset/labels/val.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (1.2GB ram): 100% 15977/15977 [02:22<00:00, 112.36it/s]\n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m6.72 anchors/target, 1.000 Best Possible Recall (BPR). Current anchors are a good fit to dataset ✅\n",
            "Plotting labels to yolov5/runs/train/exp/labels.jpg... \n",
            "Image sizes 192 train, 192 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1myolov5/runs/train/exp\u001b[0m\n",
            "Starting training for 50 epochs...\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       0/49      5.13G    0.06676    0.01751     0.1049        479        192: 100% 116/116 [01:18<00:00,  1.48it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:18<00:00,  2.45s/it]\n",
            "                   all      15977      15972    0.00404      0.967      0.028     0.0122\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       1/49      6.79G    0.04165    0.01217     0.1025        472        192: 100% 116/116 [01:13<00:00,  1.58it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:13<00:00,  2.30s/it]\n",
            "                   all      15977      15972     0.0106      0.981     0.0473     0.0261\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       2/49      6.79G    0.03484   0.009453     0.1006        462        192: 100% 116/116 [01:15<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:13<00:00,  2.31s/it]\n",
            "                   all      15977      15972     0.0116      0.977      0.119     0.0745\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       3/49      6.79G    0.02848   0.007625    0.09605        438        192: 100% 116/116 [01:15<00:00,  1.53it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.28s/it]\n",
            "                   all      15977      15972      0.583      0.147      0.163      0.112\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       4/49      6.79G    0.02594   0.006878    0.08625        462        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.27s/it]\n",
            "                   all      15977      15972      0.728      0.381       0.41      0.283\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       5/49      6.79G    0.02592   0.006704    0.06958        480        192: 100% 116/116 [01:17<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.28s/it]\n",
            "                   all      15977      15972      0.736      0.561       0.71      0.534\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       6/49      6.79G    0.02667   0.006635    0.04999        464        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.26s/it]\n",
            "                   all      15977      15972      0.944      0.752      0.902      0.679\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       7/49      6.79G    0.02711   0.006551    0.03361        441        192: 100% 116/116 [01:17<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.983      0.893      0.946      0.683\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       8/49      6.79G    0.02705   0.006449    0.02278        426        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.27s/it]\n",
            "                   all      15977      15972      0.962      0.969      0.972      0.759\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "       9/49      6.79G    0.02623   0.006374    0.01663        457        192: 100% 116/116 [01:17<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.25s/it]\n",
            "                   all      15977      15972      0.983       0.98      0.974      0.787\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      10/49      6.79G    0.02522   0.006124    0.01314        472        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:13<00:00,  2.29s/it]\n",
            "                   all      15977      15972       0.98      0.979      0.975      0.773\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      11/49      6.79G    0.02451   0.006088    0.01123        475        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.27s/it]\n",
            "                   all      15977      15972      0.983      0.981      0.976      0.786\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      12/49      6.79G    0.02378   0.006014    0.01011        446        192: 100% 116/116 [01:15<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.25s/it]\n",
            "                   all      15977      15972      0.983      0.979      0.975      0.795\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      13/49      6.79G    0.02314   0.005903   0.009116        443        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.26s/it]\n",
            "                   all      15977      15972      0.982      0.984      0.976      0.815\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      14/49      6.79G    0.02253   0.005805   0.008562        495        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.26s/it]\n",
            "                   all      15977      15972      0.984      0.982      0.976      0.822\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      15/49      6.79G    0.02216   0.005771   0.008155        472        192: 100% 116/116 [01:17<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.25s/it]\n",
            "                   all      15977      15972      0.982      0.984      0.977      0.833\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      16/49      6.79G    0.02158    0.00565   0.007609        474        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.27s/it]\n",
            "                   all      15977      15972      0.985      0.981      0.977      0.832\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      17/49      6.79G    0.02118   0.005575   0.007331        462        192: 100% 116/116 [01:15<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.27s/it]\n",
            "                   all      15977      15972      0.983      0.984      0.976       0.84\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      18/49      6.79G    0.02089   0.005568   0.006987        473        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.27s/it]\n",
            "                   all      15977      15972      0.984      0.984      0.976      0.849\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      19/49      6.79G    0.02049   0.005528   0.006753        476        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.25s/it]\n",
            "                   all      15977      15972      0.985      0.984      0.977       0.84\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      20/49      6.79G    0.02012   0.005438   0.006453        458        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.983      0.984      0.977      0.852\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      21/49      6.79G    0.01961    0.00536   0.006286        456        192: 100% 116/116 [01:17<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.986      0.983      0.977      0.853\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      22/49      6.79G     0.0194   0.005316   0.005948        495        192: 100% 116/116 [01:14<00:00,  1.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.26s/it]\n",
            "                   all      15977      15972      0.985      0.984      0.978      0.865\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      23/49      6.79G    0.01925   0.005324   0.005855        480        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:13<00:00,  2.29s/it]\n",
            "                   all      15977      15972      0.985      0.985      0.978      0.862\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      24/49      6.79G    0.01863   0.005204   0.005556        473        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.21s/it]\n",
            "                   all      15977      15972      0.983      0.985      0.977      0.862\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      25/49      6.79G    0.01841    0.00518   0.005432        463        192: 100% 116/116 [01:17<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.986      0.984      0.978      0.872\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      26/49      6.79G    0.01799   0.005161   0.005312        446        192: 100% 116/116 [01:17<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.985      0.985      0.978      0.871\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      27/49      6.79G    0.01783   0.005098   0.005078        468        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.25s/it]\n",
            "                   all      15977      15972      0.984      0.985      0.978      0.868\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      28/49      6.79G    0.01734   0.004988   0.004836        500        192: 100% 116/116 [01:15<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.26s/it]\n",
            "                   all      15977      15972      0.986      0.985      0.979      0.874\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      29/49      6.79G    0.01722    0.00499   0.004583        504        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.24s/it]\n",
            "                   all      15977      15972      0.984      0.984      0.978      0.876\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      30/49      6.79G    0.01681   0.004927   0.004509        491        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.986      0.985      0.978      0.881\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      31/49      6.79G    0.01648   0.004909   0.004503        450        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.986      0.984      0.978      0.883\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      32/49      6.79G     0.0161   0.004848   0.004303        418        192: 100% 116/116 [01:14<00:00,  1.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.25s/it]\n",
            "                   all      15977      15972      0.985      0.984      0.979      0.882\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      33/49      6.79G    0.01602    0.00482   0.004141        471        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:12<00:00,  2.26s/it]\n",
            "                   all      15977      15972      0.985      0.985      0.978      0.883\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      34/49      6.79G    0.01564   0.004778   0.003983        465        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.20s/it]\n",
            "                   all      15977      15972      0.984      0.985      0.978      0.883\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      35/49      6.79G    0.01523   0.004742   0.003939        499        192: 100% 116/116 [01:17<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.986      0.985      0.979      0.886\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      36/49      6.79G    0.01495   0.004728    0.00384        482        192: 100% 116/116 [01:17<00:00,  1.49it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.21s/it]\n",
            "                   all      15977      15972      0.987      0.984      0.979      0.888\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      37/49      6.79G    0.01458   0.004653    0.00357        456        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.986      0.985      0.979      0.888\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      38/49      6.79G    0.01422    0.00461   0.003472        447        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.986      0.985       0.98      0.888\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      39/49      6.79G    0.01397   0.004518   0.003247        458        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.987      0.984       0.98      0.892\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      40/49      6.79G    0.01354   0.004499   0.003137        451        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.21s/it]\n",
            "                   all      15977      15972      0.986      0.985       0.98      0.893\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      41/49      6.79G    0.01329    0.00444   0.003015        469        192: 100% 116/116 [01:16<00:00,  1.52it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.21s/it]\n",
            "                   all      15977      15972      0.987      0.985       0.98      0.894\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      42/49      6.79G    0.01287   0.004406   0.002877        488        192: 100% 116/116 [01:16<00:00,  1.51it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.20s/it]\n",
            "                   all      15977      15972      0.987      0.984       0.98      0.894\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      43/49      6.79G    0.01251   0.004349   0.002726        445        192: 100% 116/116 [01:14<00:00,  1.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.988      0.984       0.98      0.896\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      44/49      6.79G    0.01215   0.004287   0.002519        466        192: 100% 116/116 [01:14<00:00,  1.56it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.987      0.984       0.98      0.896\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      45/49      6.79G    0.01189   0.004265   0.002435        444        192: 100% 116/116 [01:14<00:00,  1.57it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.23s/it]\n",
            "                   all      15977      15972      0.988      0.984       0.98      0.897\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      46/49      6.79G    0.01151   0.004215    0.00224        468        192: 100% 116/116 [01:14<00:00,  1.57it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:11<00:00,  2.22s/it]\n",
            "                   all      15977      15972      0.988      0.984       0.98      0.898\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      47/49      6.79G    0.01107   0.004148   0.002038        459        192: 100% 116/116 [01:15<00:00,  1.54it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.20s/it]\n",
            "                   all      15977      15972      0.987      0.985      0.981      0.899\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      48/49      6.79G    0.01075   0.004155   0.001924        484        192: 100% 116/116 [01:17<00:00,  1.50it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:09<00:00,  2.16s/it]\n",
            "                   all      15977      15972      0.987      0.984       0.98      0.898\n",
            "\n",
            "      Epoch    GPU_mem   box_loss   obj_loss   cls_loss  Instances       Size\n",
            "      49/49      6.79G    0.01038   0.004065   0.001754        493        192: 100% 116/116 [01:14<00:00,  1.55it/s]\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:10<00:00,  2.21s/it]\n",
            "                   all      15977      15972      0.987      0.985      0.981        0.9\n",
            "\n",
            "50 epochs completed in 2.082 hours.\n",
            "Optimizer stripped from yolov5/runs/train/exp/weights/last.pt, 14.6MB\n",
            "Optimizer stripped from yolov5/runs/train/exp/weights/best.pt, 14.6MB\n",
            "\n",
            "Validating yolov5/runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 157 layers, 7220491 parameters, 0 gradients, 16.4 GFLOPs\n",
            "                 Class     Images  Instances          P          R      mAP50   mAP50-95: 100% 32/32 [01:20<00:00,  2.52s/it]\n",
            "                   all      15977      15972      0.988      0.984       0.98        0.9\n",
            "             varshithk      15977        226      0.996      0.956      0.994      0.897\n",
            "                nitish      15977        232      0.999          1      0.995      0.893\n",
            "                  ansh      15977        251      0.999          1      0.995      0.924\n",
            "              jaswanth      15977        243      0.999          1      0.995      0.949\n",
            "              jaiwanth      15977        240      0.999          1      0.995      0.967\n",
            "               aashish      15977        235      0.999          1      0.995      0.921\n",
            "                nithin      15977        236      0.999          1      0.995      0.908\n",
            "               sandeep      15977        253      0.999          1      0.995      0.894\n",
            "               swaroop      15977        239      0.999          1      0.995       0.93\n",
            "                shreya      15977        248      0.999          1      0.995      0.973\n",
            "               deeksha      15977        241      0.999          1      0.995      0.935\n",
            "               srujana      15977        217      0.999          1      0.995      0.931\n",
            "                 shiva      15977        248      0.999          1      0.995      0.938\n",
            "              animisha      15977        260      0.999          1      0.995       0.93\n",
            "               pallavi      15977        221      0.999          1      0.995      0.911\n",
            "                  neha      15977        260      0.999          1      0.995      0.917\n",
            "               sathwik      15977        246      0.999          1      0.995      0.945\n",
            "              bhavitha      15977        235      0.999          1      0.995       0.93\n",
            "              shivasai      15977        261      0.999          1      0.995      0.938\n",
            "                madhav      15977        265      0.999          1      0.995      0.922\n",
            "             harshitha      15977        260      0.991      0.992      0.988      0.938\n",
            "                 varun      15977        236      0.999          1      0.995      0.899\n",
            "            hrushikesh      15977        236      0.999          1      0.995      0.909\n",
            "               anirudh      15977        116      0.965      0.966       0.96      0.839\n",
            "                beerla      15977        236      0.999          1      0.995      0.892\n",
            "           karthikeyan      15977        227      0.999          1      0.995      0.906\n",
            "              sreepadh      15977        156      0.894      0.878      0.865      0.758\n",
            "               sreekar      15977        255      0.999          1      0.995      0.918\n",
            "            kausthubha      15977        217      0.999          1      0.995      0.935\n",
            "                 kotha      15977        135      0.733      0.569      0.556      0.452\n",
            "                  guna      15977        115       0.99      0.991      0.995      0.915\n",
            "                aditya      15977        155       0.99      0.884      0.886      0.817\n",
            "              aashishy      15977         85      0.964      0.956      0.948        0.8\n",
            "               shubang      15977        245      0.987      0.988      0.986      0.899\n",
            "                girish      15977        263      0.999          1      0.995      0.896\n",
            "                mithra      15977        234      0.999          1      0.995      0.924\n",
            "        anirudhvuppala      15977        135      0.998          1      0.995      0.923\n",
            "       anumasupranathi      15977        241      0.999          1      0.995      0.911\n",
            "        bottusameeksha      15977        190       0.92      0.921      0.931      0.798\n",
            "               bvarsha      15977        232      0.999          1      0.995      0.866\n",
            " chaitanyayavallakatla      15977        119      0.998          1      0.995      0.885\n",
            "      chirandastejaswi      15977        259      0.999          1      0.995      0.957\n",
            "        dhanushkumetha      15977        159      0.999          1      0.995      0.909\n",
            "        gayathrialladi      15977        251      0.999          1      0.995      0.913\n",
            "             gsrikaran      15977        175      0.999          1      0.995      0.925\n",
            "                harish      15977        244      0.999          1      0.995      0.965\n",
            "        harshithakotla      15977        162      0.992      0.994      0.994      0.917\n",
            "       harshithmutyapu      15977        100      0.998          1      0.995      0.914\n",
            "           kaipabhuvan      15977        220      0.968      0.968      0.964      0.888\n",
            "         karthikbehara      15977        199      0.999          1      0.995       0.92\n",
            "               kinshuk      15977        186      0.999          1      0.995      0.941\n",
            "        krishnaadithya      15977        208      0.989       0.99      0.986      0.944\n",
            "         kushaldalavai      15977        206      0.973      0.961      0.973      0.905\n",
            "          kvlpraphulla      15977        187      0.978      0.979      0.961      0.827\n",
            "            lakkevivek      15977        205      0.999          1      0.995      0.868\n",
            "           mangasindhu      15977        141      0.992          1      0.995      0.929\n",
            "               manitej      15977        235      0.999          1      0.995      0.975\n",
            "          meghanavanam      15977        240      0.962      0.963      0.983      0.859\n",
            "                pkavya      15977        162      0.999          1      0.995      0.858\n",
            "        prashanthkumar      15977        103      0.969      0.981      0.975       0.83\n",
            "        purumaniashish      15977        238      0.999          1      0.995        0.9\n",
            "           raghavateja      15977        211      0.994      0.995      0.994       0.94\n",
            "          rakeshchanda      15977        120      0.998          1      0.995      0.905\n",
            "      saimuralidharraj      15977        228      0.999          1      0.995      0.937\n",
            "              saisanoj      15977        254      0.999          1      0.995      0.942\n",
            "        samuelmathangi      15977        171      0.999          1      0.995      0.872\n",
            "           sasanksarma      15977        226      0.999          1      0.995      0.935\n",
            "  sathvikramachandruni      15977        153      0.999          1      0.995      0.899\n",
            "         sathvikuppala      15977        221      0.999          1      0.995      0.942\n",
            "         shivamanideep      15977        253      0.999          1      0.995      0.914\n",
            "          sirichandana      15977        266      0.999          1      0.995      0.948\n",
            "      srikarikarnakoti      15977        192      0.984      0.984      0.976      0.887\n",
            "         tejasrigurram      15977        220      0.985      0.977      0.964      0.888\n",
            "          varshiniyara      15977        149      0.946      0.946      0.944      0.824\n",
            "              vmeghana      15977        238      0.974      0.975       0.96      0.866\n",
            "     yashaswiniveldana      15977        245      0.999          1      0.995      0.904\n",
            "Results saved to \u001b[1myolov5/runs/train/exp\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dE0oBYFChJ-k"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}