Number_plate_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yNveqeA1KXGy"
      },
      "source": [
        "# Step 1: Install Requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTvDNSILZoN9",
        "outputId": "7d47532a-749f-4ac9-df34-a68fc97f39d1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 12192, done.\u001b[K\n",
            "remote: Total 12192 (delta 0), reused 0 (delta 0), pack-reused 12192\u001b[K\n",
            "Receiving objects: 100% (12192/12192), 11.91 MiB | 22.37 MiB/s, done.\n",
            "Resolving deltas: 100% (8441/8441), done.\n",
            "/content/yolov5/yolov5\n",
            "Setup complete. Using torch 1.11.0+cu113 (Tesla T4)\n"
          ]
        }
      ],
      "source": [
        "#clone YOLOv5 and \n",
        "!git clone https://github.com/ultralytics/yolov5  # clone repo\n",
        "%cd yolov5\n",
        "%pip install -qr requirements.txt # install dependencies\n",
        "%pip install -q roboflow\n",
        "\n",
        "import torch\n",
        "import os\n",
        "from IPython.display import Image, clear_output  # to display images\n",
        "\n",
        "print(f\"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R2wGvjd4Z_92",
        "outputId": "1e1f994b-3b0e-43a4-cf54-832cfd5aec74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "upload and label your dataset, and get an API KEY here: https://app.roboflow.com/?model=yolov5&ref=ultralytics\n"
          ]
        }
      ],
      "source": [
        "from roboflow import Roboflow\n",
        "rf = Roboflow(model_format=\"yolov5\", notebook=\"ultralytics\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "2jjT5uIHo6l5"
      },
      "outputs": [],
      "source": [
        "# set up environment\n",
        "os.environ[\"DATASET_DIRECTORY\"] = \"/content/datasets\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwJcaoPGF4VI",
        "outputId": "5300fdd5-c779-4a0e-f12b-d8fd4480a75c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: roboflow in /usr/local/lib/python3.7/dist-packages (0.2.4)\n",
            "Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.7/dist-packages (from roboflow) (1.20.3)\n",
            "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.7/dist-packages (from roboflow) (0.20.0)\n",
            "Requirement already satisfied: urllib3==1.26.6 in /usr/local/lib/python3.7/dist-packages (from roboflow) (1.26.6)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from roboflow) (3.2.2)\n",
            "Requirement already satisfied: tqdm>=4.41.0 in /usr/local/lib/python3.7/dist-packages (from roboflow) (4.64.0)\n",
            "Requirement already satisfied: opencv-python>=4.1.2 in /usr/local/lib/python3.7/dist-packages (from roboflow) (4.1.2.30)\n",
            "Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.7/dist-packages (from roboflow) (6.0)\n",
            "Requirement already satisfied: chardet==4.0.0 in /usr/local/lib/python3.7/dist-packages (from roboflow) (4.0.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from roboflow) (1.15.0)\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.7/dist-packages (from roboflow) (3.2)\n",
            "Requirement already satisfied: requests-toolbelt in /usr/local/lib/python3.7/dist-packages (from roboflow) (0.9.1)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from roboflow) (2.8.2)\n",
            "Requirement already satisfied: certifi==2021.5.30 in /usr/local/lib/python3.7/dist-packages (from roboflow) (2021.5.30)\n",
            "Requirement already satisfied: pyparsing==2.4.7 in /usr/local/lib/python3.7/dist-packages (from roboflow) (2.4.7)\n",
            "Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.7/dist-packages (from roboflow) (9.1.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from roboflow) (2.27.1)\n",
            "Requirement already satisfied: cycler==0.10.0 in /usr/local/lib/python3.7/dist-packages (from roboflow) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver==1.3.1 in /usr/local/lib/python3.7/dist-packages (from roboflow) (1.3.1)\n",
            "Requirement already satisfied: idna==2.10 in /usr/local/lib/python3.7/dist-packages (from roboflow) (2.10)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->roboflow) (2.0.12)\n",
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n",
            "Downloading Dataset Version Zip in /content/datasets/License-plate-detection-1 to yolov5pytorch: 100% [62975368 / 62975368] bytes\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Extracting Dataset Version Zip to /content/datasets/License-plate-detection-1 in yolov5pytorch:: 100%|██████████| 2108/2108 [00:02<00:00, 798.98it/s] \n"
          ]
        }
      ],
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"cVEtmQca6JMsH0rWK9rV\")\n",
        "project = rf.workspace(\"sudips-workspace\").project(\"license-plate-detection-x9ngg\")\n",
        "dataset = project.version(1).download(\"yolov5\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7yAi9hd-T4B"
      },
      "source": [
        "# Step 3: Train Our Custom YOLOv5 model\n",
        "\n",
        "Here, we are able to pass a number of arguments:\n",
        "- **img:** define input image size\n",
        "- **batch:** determine batch size\n",
        "- **epochs:** define the number of training epochs. (Note: often, 3000+ are common here!)\n",
        "- **data:** Our dataset locaiton is saved in the `dataset.location`\n",
        "- **weights:** specify a path to weights to start transfer learning from. Here we choose the generic COCO pretrained checkpoint.\n",
        "- **cache:** cache images for faster training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eaFNnxLJbq4J",
        "outputId": "92397375-5e8a-4b6c-c586-57afd0ec7686"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=/content/datasets/License-plate-detection-1/data.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=150, batch_size=16, imgsz=416, 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=runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, 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 🚀 v6.1-246-g2dd3db0 Python-3.7.13 torch-1.11.0+cu113 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[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt to yolov5s.pt...\n",
            "100% 14.1M/14.1M [00:00<00:00, 207MB/s]\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=1\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     16182  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model summary: 270 layers, 7022326 parameters, 7022326 gradients, 15.8 GFLOPs\n",
            "\n",
            "Transferred 343/349 items from yolov5s.pt\n",
            "\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n",
            "Scaled weight_decay = 0.0005\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '/content/datasets/License-plate-detection-1/train/labels.cache' images and labels... 914 found, 0 missing, 0 empty, 0 corrupt: 100% 914/914 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.5GB ram): 100% 914/914 [00:02<00:00, 348.35it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '/content/datasets/License-plate-detection-1/valid/labels.cache' images and labels... 92 found, 0 missing, 0 empty, 0 corrupt: 100% 92/92 [00:00<?, ?it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.0GB ram): 100% 92/92 [00:00<00:00, 193.35it/s]\n",
            "Plotting labels to runs/train/exp/labels.jpg... \n",
            "\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0m4.35 anchors/target, 0.948 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve...\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mWARNING: Extremely small objects found: 27 of 1371 labels are < 3 pixels in size\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mRunning kmeans for 9 anchors on 1371 points...\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mEvolving anchors with Genetic Algorithm: fitness = 0.7926: 100% 1000/1000 [00:00<00:00, 2039.51it/s]\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mthr=0.25: 0.9934 best possible recall, 4.67 anchors past thr\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mn=9, img_size=416, metric_all=0.333/0.794-mean/best, past_thr=0.532-mean: 7,4, 18,12, 30,21, 48,33, 64,46, 83,74, 115,105, 202,162, 293,266\n",
            "\u001b[34m\u001b[1mAutoAnchor: \u001b[0mDone ✅ (optional: update model *.yaml to use these anchors in the future)\n",
            "Image sizes 416 train, 416 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to \u001b[1mruns/train/exp\u001b[0m\n",
            "Starting training for 150 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     0/149     1.58G    0.1138   0.01631         0         3       416: 100% 58/58 [00:11<00:00,  4.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  4.62it/s]\n",
            "                 all         92        147    0.00181       0.34    0.00482    0.00128\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     1/149     1.94G   0.08713   0.01647         0         1       416: 100% 58/58 [00:09<00:00,  6.06it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.18it/s]\n",
            "                 all         92        147      0.147      0.106     0.0436     0.0129\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     2/149     1.94G   0.07402   0.01544         0         2       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.418      0.265      0.284      0.109\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     3/149     1.94G   0.06539   0.01503         0         2       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.59it/s]\n",
            "                 all         92        147      0.454      0.367      0.379      0.129\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     4/149     1.94G   0.05884   0.01312         0         4       416: 100% 58/58 [00:08<00:00,  6.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  4.07it/s]\n",
            "                 all         92        147      0.664       0.51      0.529      0.197\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     5/149     1.94G   0.05448   0.01216         0         7       416: 100% 58/58 [00:08<00:00,  6.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.59it/s]\n",
            "                 all         92        147      0.727      0.543      0.588      0.235\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     6/149     1.94G   0.05165   0.01112         0         6       416: 100% 58/58 [00:08<00:00,  6.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "                 all         92        147      0.624       0.51      0.416      0.129\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     7/149     1.94G    0.0498   0.01068         0         4       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147      0.854      0.599      0.649      0.276\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     8/149     1.94G   0.04692   0.01019         0         4       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "                 all         92        147      0.837      0.653      0.731      0.359\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     9/149     1.94G    0.0465  0.009631         0         2       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "                 all         92        147      0.855      0.646       0.71      0.314\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    10/149     1.94G   0.04529  0.009164         0         6       416: 100% 58/58 [00:08<00:00,  6.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.63it/s]\n",
            "                 all         92        147      0.838      0.669      0.702      0.247\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    11/149     1.94G   0.04303  0.009472         0         3       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "                 all         92        147      0.918      0.694      0.752      0.368\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    12/149     1.94G   0.04337  0.009229         0         6       416: 100% 58/58 [00:08<00:00,  6.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.60it/s]\n",
            "                 all         92        147      0.803      0.638      0.679      0.273\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    13/149     1.94G   0.04204  0.008601         0         6       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.62it/s]\n",
            "                 all         92        147      0.933      0.741      0.818      0.357\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    14/149     1.94G    0.0402  0.008682         0         5       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "                 all         92        147      0.876      0.769      0.828      0.433\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    15/149     1.94G   0.04062   0.00829         0         3       416: 100% 58/58 [00:08<00:00,  6.71it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "                 all         92        147      0.884      0.735      0.797       0.36\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    16/149     1.94G   0.03932  0.008069         0         5       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.888      0.758      0.821      0.395\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    17/149     1.94G   0.03932  0.008013         0         3       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "                 all         92        147      0.956      0.728      0.829      0.413\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    18/149     1.94G   0.03923  0.008034         0         2       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147       0.89      0.718      0.807      0.358\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    19/149     1.94G   0.03734  0.007757         0         4       416: 100% 58/58 [00:08<00:00,  6.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "                 all         92        147      0.948      0.749      0.851      0.406\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    20/149     1.94G   0.03725   0.00796         0         3       416: 100% 58/58 [00:08<00:00,  6.79it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.958      0.767      0.853      0.359\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    21/149     1.94G   0.03773  0.007874         0         3       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.61it/s]\n",
            "                 all         92        147      0.887      0.748      0.818       0.35\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    22/149     1.94G   0.03726   0.00793         0         5       416: 100% 58/58 [00:08<00:00,  6.85it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.38it/s]\n",
            "                 all         92        147      0.983      0.769      0.856      0.466\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    23/149     1.94G   0.03809  0.007673         0         1       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.913      0.787      0.853      0.411\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    24/149     1.94G   0.03611  0.007672         0         4       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "                 all         92        147      0.878      0.721        0.8      0.368\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    25/149     1.94G   0.03464  0.007366         0         4       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "                 all         92        147      0.956      0.789      0.871      0.436\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    26/149     1.94G   0.03417    0.0073         0         4       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "                 all         92        147      0.964      0.739      0.871      0.453\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    27/149     1.94G   0.03466  0.007523         0         3       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.54it/s]\n",
            "                 all         92        147      0.949      0.767      0.847      0.398\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    28/149     1.94G   0.03458  0.007742         0        15       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "                 all         92        147      0.936      0.776      0.881      0.437\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    29/149     1.94G   0.03391  0.007282         0         1       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "                 all         92        147      0.935      0.803      0.866      0.447\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    30/149     1.94G   0.03429  0.007383         0         3       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "                 all         92        147      0.921      0.798      0.867      0.426\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    31/149     1.94G   0.03318   0.00709         0         3       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  4.58it/s]\n",
            "                 all         92        147      0.949      0.789      0.868      0.452\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    32/149     1.94G    0.0332  0.007163         0         2       416: 100% 58/58 [00:09<00:00,  6.44it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "                 all         92        147      0.919      0.851      0.914       0.42\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    33/149     1.94G    0.0322  0.007219         0         0       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.63it/s]\n",
            "                 all         92        147      0.885      0.823      0.856      0.395\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    34/149     1.94G   0.03292  0.007215         0        10       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "                 all         92        147      0.905      0.816      0.873      0.467\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    35/149     1.94G   0.03251  0.007015         0         1       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.947      0.844      0.911      0.458\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    36/149     1.94G   0.03243  0.006718         0         2       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "                 all         92        147      0.925      0.836      0.903      0.457\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    37/149     1.94G   0.03122   0.00662         0         2       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.941      0.796      0.878      0.422\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    38/149     1.94G   0.03122  0.006974         0         5       416: 100% 58/58 [00:08<00:00,  6.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "                 all         92        147      0.945      0.837      0.898      0.475\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    39/149     1.94G   0.03099  0.006791         0         4       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.56it/s]\n",
            "                 all         92        147      0.903      0.884      0.896      0.474\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    40/149     1.94G   0.03134  0.006804         0         3       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.62it/s]\n",
            "                 all         92        147      0.924      0.827      0.902       0.43\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    41/149     1.94G   0.03172   0.00681         0         5       416: 100% 58/58 [00:08<00:00,  6.85it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "                 all         92        147       0.93      0.776      0.863      0.402\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    42/149     1.94G   0.03117  0.006831         0         3       416: 100% 58/58 [00:08<00:00,  6.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.925      0.833      0.878      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    43/149     1.94G   0.03006  0.006744         0         3       416: 100% 58/58 [00:08<00:00,  6.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.55it/s]\n",
            "                 all         92        147      0.943      0.792      0.863      0.443\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    44/149     1.94G    0.0299  0.006471         0         3       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "                 all         92        147      0.939      0.832      0.883       0.44\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    45/149     1.94G   0.03024  0.006989         0         4       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.892      0.837      0.873      0.478\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    46/149     1.94G   0.02884  0.006814         0         2       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.58it/s]\n",
            "                 all         92        147      0.945      0.815      0.879      0.439\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    47/149     1.94G   0.02995  0.006698         0         6       416: 100% 58/58 [00:08<00:00,  6.95it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.62it/s]\n",
            "                 all         92        147      0.959       0.83      0.891      0.446\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    48/149     1.94G   0.03003  0.006615         0        10       416: 100% 58/58 [00:08<00:00,  6.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.59it/s]\n",
            "                 all         92        147      0.968      0.821      0.906      0.441\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    49/149     1.94G    0.0296  0.006496         0         2       416: 100% 58/58 [00:08<00:00,  6.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.57it/s]\n",
            "                 all         92        147      0.963      0.823      0.883      0.462\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    50/149     1.94G   0.02948  0.006697         0         8       416: 100% 58/58 [00:08<00:00,  6.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "                 all         92        147      0.944      0.844      0.894      0.435\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    51/149     1.94G   0.02933  0.006047         0         3       416: 100% 58/58 [00:08<00:00,  6.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "                 all         92        147      0.938      0.828      0.878      0.473\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    52/149     1.94G   0.02882   0.00635         0         5       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.56it/s]\n",
            "                 all         92        147       0.96      0.818      0.886      0.438\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    53/149     1.94G   0.02863  0.006314         0         5       416: 100% 58/58 [00:08<00:00,  6.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "                 all         92        147      0.944      0.803      0.888      0.419\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    54/149     1.94G    0.0288  0.006305         0         4       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.46it/s]\n",
            "                 all         92        147      0.918       0.85      0.887      0.461\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    55/149     1.94G   0.02886  0.006561         0         5       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.60it/s]\n",
            "                 all         92        147      0.902      0.881      0.906      0.464\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    56/149     1.94G   0.02751  0.006518         0         1       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.57it/s]\n",
            "                 all         92        147       0.91      0.864      0.915      0.507\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    57/149     1.94G   0.02914  0.006312         0         4       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.947      0.844      0.908      0.491\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    58/149     1.94G    0.0287  0.006291         0         7       416: 100% 58/58 [00:08<00:00,  6.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.52it/s]\n",
            "                 all         92        147      0.923       0.83      0.887      0.417\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    59/149     1.94G   0.02747  0.006372         0         4       416: 100% 58/58 [00:08<00:00,  6.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "                 all         92        147      0.963       0.83      0.904      0.468\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    60/149     1.94G    0.0272  0.006234         0         3       416: 100% 58/58 [00:08<00:00,  6.95it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.899      0.851      0.906      0.454\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    61/149     1.94G   0.02731  0.006365         0         4       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.925      0.857        0.9      0.464\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    62/149     1.94G    0.0267  0.006181         0         6       416: 100% 58/58 [00:08<00:00,  6.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.914      0.864      0.906      0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    63/149     1.94G   0.02661   0.00586         0         1       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.53it/s]\n",
            "                 all         92        147      0.947      0.864      0.909      0.466\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    64/149     1.94G   0.02606  0.006088         0         8       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "                 all         92        147      0.976      0.822      0.905      0.485\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    65/149     1.94G   0.02704  0.006165         0         7       416: 100% 58/58 [00:09<00:00,  6.21it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.53it/s]\n",
            "                 all         92        147      0.933      0.878      0.923      0.506\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    66/149     1.94G   0.02656  0.005863         0         1       416: 100% 58/58 [00:08<00:00,  6.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147      0.949      0.898       0.94      0.522\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    67/149     1.94G   0.02617  0.005903         0         4       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.54it/s]\n",
            "                 all         92        147      0.919       0.85      0.898       0.51\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    68/149     1.94G   0.02622  0.006133         0         4       416: 100% 58/58 [00:08<00:00,  6.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.916      0.878      0.911      0.468\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    69/149     1.94G   0.02608  0.006047         0         5       416: 100% 58/58 [00:08<00:00,  6.91it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "                 all         92        147      0.941      0.864      0.921      0.481\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    70/149     1.94G   0.02586  0.006116         0         7       416: 100% 58/58 [00:08<00:00,  6.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "                 all         92        147       0.95      0.878      0.925       0.53\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    71/149     1.94G   0.02612  0.006108         0         6       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.52it/s]\n",
            "                 all         92        147      0.936      0.864      0.912      0.488\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    72/149     1.94G   0.02528  0.006054         0         1       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.59it/s]\n",
            "                 all         92        147      0.927       0.86      0.913      0.477\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    73/149     1.94G   0.02513  0.006022         0         9       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.56it/s]\n",
            "                 all         92        147      0.936      0.857      0.918      0.495\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    74/149     1.94G   0.02479  0.005891         0         6       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147       0.97      0.869      0.935        0.5\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    75/149     1.94G   0.02506  0.006166         0         3       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147      0.936      0.884      0.937      0.503\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    76/149     1.94G   0.02436    0.0058         0         5       416: 100% 58/58 [00:08<00:00,  6.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.57it/s]\n",
            "                 all         92        147      0.956      0.877      0.922      0.499\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    77/149     1.94G   0.02481   0.00576         0         4       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "                 all         92        147      0.954      0.849      0.908      0.497\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    78/149     1.94G    0.0243  0.005845         0         4       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "                 all         92        147      0.967      0.878      0.929       0.51\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    79/149     1.94G   0.02475  0.005895         0         2       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.931      0.884      0.928      0.518\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    80/149     1.94G   0.02435  0.005675         0         5       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "                 all         92        147      0.906      0.919       0.94      0.518\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    81/149     1.94G   0.02406   0.00581         0         7       416: 100% 58/58 [00:08<00:00,  6.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.59it/s]\n",
            "                 all         92        147      0.935      0.876      0.931      0.495\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    82/149     1.94G   0.02353  0.005696         0         3       416: 100% 58/58 [00:08<00:00,  6.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.52it/s]\n",
            "                 all         92        147      0.928      0.872      0.906      0.478\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    83/149     1.94G   0.02339  0.005581         0         5       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.38it/s]\n",
            "                 all         92        147      0.949      0.883      0.929      0.517\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    84/149     1.94G   0.02392  0.005723         0         4       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "                 all         92        147      0.925      0.884      0.934      0.485\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    85/149     1.94G   0.02329  0.005817         0         3       416: 100% 58/58 [00:08<00:00,  6.92it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "                 all         92        147      0.951      0.905      0.944      0.504\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    86/149     1.94G   0.02366   0.00568         0         3       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "                 all         92        147      0.955      0.891      0.931      0.523\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    87/149     1.94G    0.0229  0.005626         0         3       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.60it/s]\n",
            "                 all         92        147      0.936      0.898      0.924      0.483\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    88/149     1.94G   0.02284  0.005455         0         1       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147      0.936      0.891      0.926      0.482\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    89/149     1.94G   0.02239  0.005668         0         4       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.60it/s]\n",
            "                 all         92        147      0.949      0.871      0.924      0.471\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    90/149     1.94G   0.02268  0.005566         0         4       416: 100% 58/58 [00:08<00:00,  6.81it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "                 all         92        147      0.941      0.898      0.934      0.496\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    91/149     1.94G   0.02237  0.005444         0         4       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.61it/s]\n",
            "                 all         92        147      0.897      0.885      0.925      0.506\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    92/149     1.94G   0.02314  0.005524         0         2       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "                 all         92        147      0.925      0.871      0.931      0.492\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    93/149     1.94G   0.02326  0.005465         0         3       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.957      0.904      0.944      0.509\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    94/149     1.94G   0.02257  0.005399         0         6       416: 100% 58/58 [00:08<00:00,  6.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.956      0.896      0.942      0.528\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    95/149     1.94G   0.02222  0.005313         0         0       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.955      0.876      0.923      0.513\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    96/149     1.94G   0.02247  0.005693         0         1       416: 100% 58/58 [00:08<00:00,  6.95it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.32it/s]\n",
            "                 all         92        147      0.942      0.878      0.935      0.497\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    97/149     1.94G   0.02239  0.005477         0         5       416: 100% 58/58 [00:09<00:00,  6.19it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "                 all         92        147       0.94      0.898      0.953      0.504\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    98/149     1.94G   0.02146   0.00563         0         5       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.47it/s]\n",
            "                 all         92        147      0.962      0.864      0.925       0.53\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "    99/149     1.94G   0.02203  0.005202         0         3       416: 100% 58/58 [00:08<00:00,  6.79it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.53it/s]\n",
            "                 all         92        147       0.97      0.871      0.945      0.493\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   100/149     1.94G    0.0216   0.00508         0         2       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.948      0.898      0.936      0.494\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   101/149     1.94G   0.02165  0.005283         0         6       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.44it/s]\n",
            "                 all         92        147      0.949       0.89      0.936      0.514\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   102/149     1.94G   0.02198  0.005116         0         2       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.24it/s]\n",
            "                 all         92        147      0.962      0.852       0.93      0.524\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   103/149     1.94G   0.02177  0.005395         0         3       416: 100% 58/58 [00:08<00:00,  6.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "                 all         92        147      0.955      0.868      0.933      0.513\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   104/149     1.94G   0.02195  0.005406         0         5       416: 100% 58/58 [00:08<00:00,  6.77it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.31it/s]\n",
            "                 all         92        147      0.951      0.864      0.938      0.508\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   105/149     1.94G    0.0208  0.005235         0         3       416: 100% 58/58 [00:08<00:00,  6.54it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.53it/s]\n",
            "                 all         92        147       0.97      0.878      0.935      0.504\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   106/149     1.94G   0.02143   0.00548         0         4       416: 100% 58/58 [00:08<00:00,  6.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.42it/s]\n",
            "                 all         92        147      0.975      0.864      0.927      0.505\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   107/149     1.94G   0.02167  0.005295         0        10       416: 100% 58/58 [00:08<00:00,  6.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "                 all         92        147      0.971      0.884      0.925      0.507\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   108/149     1.94G   0.02097   0.00531         0         4       416: 100% 58/58 [00:08<00:00,  6.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.31it/s]\n",
            "                 all         92        147      0.934      0.912      0.938      0.524\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   109/149     1.94G   0.02057  0.005251         0         8       416: 100% 58/58 [00:08<00:00,  6.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.51it/s]\n",
            "                 all         92        147      0.971      0.891      0.933      0.542\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   110/149     1.94G   0.02106  0.005245         0         7       416: 100% 58/58 [00:08<00:00,  6.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "                 all         92        147      0.958      0.898      0.929      0.525\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   111/149     1.94G   0.02088  0.005162         0        10       416: 100% 58/58 [00:08<00:00,  6.80it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.949      0.891      0.931      0.499\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   112/149     1.94G   0.02098  0.004958         0         2       416: 100% 58/58 [00:08<00:00,  6.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "                 all         92        147      0.963      0.885      0.929      0.509\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   113/149     1.94G   0.02006  0.005099         0         4       416: 100% 58/58 [00:08<00:00,  6.75it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.56it/s]\n",
            "                 all         92        147      0.977      0.898      0.948      0.519\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   114/149     1.94G   0.02029  0.005095         0         5       416: 100% 58/58 [00:08<00:00,  6.78it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "                 all         92        147      0.968      0.891      0.945      0.513\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   115/149     1.94G   0.02047  0.004918         0         4       416: 100% 58/58 [00:08<00:00,  6.81it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "                 all         92        147      0.928      0.898      0.928      0.521\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   116/149     1.94G   0.02037  0.005149         0         4       416: 100% 58/58 [00:08<00:00,  6.78it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.46it/s]\n",
            "                 all         92        147      0.965      0.884      0.941      0.519\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   117/149     1.94G   0.02045  0.004814         0         2       416: 100% 58/58 [00:08<00:00,  6.78it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "                 all         92        147      0.932      0.884      0.929      0.515\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   118/149     1.94G   0.02047  0.004982         0         2       416: 100% 58/58 [00:08<00:00,  6.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.29it/s]\n",
            "                 all         92        147       0.94      0.884       0.92       0.51\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   119/149     1.94G   0.01925   0.00493         0         4       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147      0.949      0.898      0.931      0.531\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   120/149     1.94G   0.01975   0.00489         0         2       416: 100% 58/58 [00:08<00:00,  6.85it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.34it/s]\n",
            "                 all         92        147      0.964      0.901      0.945      0.537\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   121/149     1.94G   0.01956  0.004889         0         0       416: 100% 58/58 [00:08<00:00,  6.88it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "                 all         92        147      0.975      0.912      0.947      0.522\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   122/149     1.94G   0.01986   0.00508         0         3       416: 100% 58/58 [00:08<00:00,  6.81it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.17it/s]\n",
            "                 all         92        147      0.947      0.905      0.938      0.533\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   123/149     1.94G   0.01926  0.004886         0         5       416: 100% 58/58 [00:08<00:00,  6.84it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.30it/s]\n",
            "                 all         92        147       0.96      0.912      0.947       0.52\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   124/149     1.94G   0.01917  0.004878         0         4       416: 100% 58/58 [00:08<00:00,  6.86it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.33it/s]\n",
            "                 all         92        147      0.962      0.912      0.942      0.528\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   125/149     1.94G   0.01921  0.004895         0         8       416: 100% 58/58 [00:08<00:00,  6.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.54it/s]\n",
            "                 all         92        147      0.943      0.899      0.939      0.519\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   126/149     1.94G   0.01861  0.004808         0         5       416: 100% 58/58 [00:08<00:00,  6.89it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.54it/s]\n",
            "                 all         92        147      0.964      0.917      0.938      0.517\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   127/149     1.94G   0.01943  0.005061         0         4       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.54it/s]\n",
            "                 all         92        147      0.978      0.912      0.953      0.551\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   128/149     1.94G   0.01898  0.005233         0         8       416: 100% 58/58 [00:09<00:00,  6.23it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.39it/s]\n",
            "                 all         92        147      0.963      0.912      0.953      0.539\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   129/149     1.94G   0.01876  0.004915         0         5       416: 100% 58/58 [00:08<00:00,  6.87it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.31it/s]\n",
            "                 all         92        147      0.939      0.891      0.932       0.52\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   130/149     1.94G   0.01901  0.004857         0         6       416: 100% 58/58 [00:08<00:00,  6.83it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.38it/s]\n",
            "                 all         92        147      0.968      0.918      0.953      0.507\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   131/149     1.94G    0.0186  0.004798         0         3       416: 100% 58/58 [00:08<00:00,  6.77it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.45it/s]\n",
            "                 all         92        147      0.964      0.909      0.953      0.518\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   132/149     1.94G   0.01803  0.004835         0         2       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "                 all         92        147      0.945      0.927      0.951      0.527\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   133/149     1.94G   0.01804  0.004644         0         3       416: 100% 58/58 [00:08<00:00,  6.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.52it/s]\n",
            "                 all         92        147      0.971      0.904      0.944      0.531\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   134/149     1.94G   0.01857  0.004781         0         5       416: 100% 58/58 [00:08<00:00,  6.90it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.36it/s]\n",
            "                 all         92        147      0.948      0.918      0.944      0.528\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   135/149     1.94G   0.01771  0.004572         0         2       416: 100% 58/58 [00:08<00:00,  6.82it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.50it/s]\n",
            "                 all         92        147      0.971      0.911      0.951      0.538\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   136/149     1.94G    0.0179  0.004773         0         1       416: 100% 58/58 [00:08<00:00,  6.72it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "                 all         92        147      0.971      0.912      0.958      0.534\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   137/149     1.94G   0.01794  0.004693         0         5       416: 100% 58/58 [00:08<00:00,  6.77it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.40it/s]\n",
            "                 all         92        147      0.971      0.904      0.952      0.537\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   138/149     1.94G     0.018  0.004756         0         2       416: 100% 58/58 [00:08<00:00,  6.75it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.28it/s]\n",
            "                 all         92        147      0.956      0.891      0.947      0.544\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   139/149     1.94G   0.01806  0.004744         0         5       416: 100% 58/58 [00:08<00:00,  6.63it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "                 all         92        147      0.947      0.932      0.964      0.538\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   140/149     1.94G   0.01804  0.004728         0         3       416: 100% 58/58 [00:08<00:00,  6.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.43it/s]\n",
            "                 all         92        147      0.951      0.931      0.956       0.55\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   141/149     1.94G   0.01767  0.004638         0         5       416: 100% 58/58 [00:08<00:00,  6.55it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "                 all         92        147      0.971      0.915      0.951      0.546\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   142/149     1.94G   0.01784  0.004712         0         4       416: 100% 58/58 [00:08<00:00,  6.66it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.20it/s]\n",
            "                 all         92        147      0.965      0.918      0.956       0.55\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   143/149     1.94G   0.01809  0.004731         0         1       416: 100% 58/58 [00:08<00:00,  6.66it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.37it/s]\n",
            "                 all         92        147      0.971      0.915      0.952      0.537\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   144/149     1.94G   0.01772  0.004738         0         1       416: 100% 58/58 [00:08<00:00,  6.67it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.11it/s]\n",
            "                 all         92        147      0.974      0.912      0.952      0.545\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   145/149     1.94G    0.0181  0.004749         0         6       416: 100% 58/58 [00:08<00:00,  6.53it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.41it/s]\n",
            "                 all         92        147      0.961      0.898      0.941      0.539\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   146/149     1.94G   0.01725  0.004742         0         6       416: 100% 58/58 [00:08<00:00,  6.69it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.48it/s]\n",
            "                 all         92        147      0.971      0.912      0.952      0.538\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   147/149     1.94G   0.01767  0.004629         0         2       416: 100% 58/58 [00:08<00:00,  6.73it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.35it/s]\n",
            "                 all         92        147      0.973      0.912      0.954      0.545\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   148/149     1.94G   0.01739  0.004525         0         3       416: 100% 58/58 [00:08<00:00,  6.74it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.23it/s]\n",
            "                 all         92        147      0.968      0.912      0.953      0.528\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "   149/149     1.94G   0.01749  0.004505         0         4       416: 100% 58/58 [00:08<00:00,  6.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:00<00:00,  5.49it/s]\n",
            "                 all         92        147      0.971      0.918      0.956      0.542\n",
            "\n",
            "150 epochs completed in 0.395 hours.\n",
            "Optimizer stripped from runs/train/exp/weights/last.pt, 14.3MB\n",
            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.3MB\n",
            "\n",
            "Validating runs/train/exp/weights/best.pt...\n",
            "Fusing layers... \n",
            "Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 3/3 [00:02<00:00,  1.25it/s]\n",
            "                 all         92        147      0.978      0.912      0.954      0.551\n",
            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python train.py --img 416 --batch 16 --epochs 150 --data {dataset.location}/data.yaml --weights yolov5s.pt --cache"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AcIRLQOlA14A"
      },
      "source": [
        "# Evaluate Custom YOLOv5 Detector Performance\n",
        "Training losses and performance metrics are saved to Tensorboard and also to a logfile.\n",
        "\n",
        "If you are new to these metrics, the one you want to focus on is `mAP_0.5` - learn more about mean average precision "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "1jS9_BxdBBHL"
      },
      "outputs": [],
      "source": [
        "# Start tensorboard\n",
        "# Launch after you have started training\n",
        "# logs save in the folder \"runs\"\n",
        "#%load_ext tensorboard\n",
        "#%tensorboard --logdir runs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jtmS7_TXFsT3"
      },
      "source": [
        "#Run Inference  With Trained Weights\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TWjjiBcic3Vz",
        "outputId": "b35d56e9-a65b-4c95-9d06-4bed134b8f51"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['runs/train/exp/weights/best.pt'], source=/content/image.jpg, data=data/coco128.yaml, imgsz=[416, 416], conf_thres=0.1, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n",
            "YOLOv5 🚀 v6.1-246-g2dd3db0 Python-3.7.13 torch-1.11.0+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
            "\n",
            "Fusing layers... \n",
            "Model summary: 213 layers, 7012822 parameters, 0 gradients, 15.8 GFLOPs\n",
            "image 1/1 /content/image.jpg: 320x416 1 License-Plate, Done. (0.015s)\n",
            "Speed: 0.4ms pre-process, 14.5ms inference, 1.2ms NMS per image at shape (1, 3, 416, 416)\n",
            "Results saved to \u001b[1mruns/detect/exp2\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "!python detect.py --weights runs/train/exp/weights/best.pt --img 416 --conf 0.1 --source /content/image.jpg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "ZbUn4_b9GCKO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "outputId": "0478d126-4621-4f88-e789-014b1b16ab05"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Image object>"
            ],
            "image/jpeg": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "#display inference on ALL test images\n",
        "\n",
        "import glob\n",
        "from IPython.display import Image, display\n",
        "\n",
        "for imageName in glob.glob('/content/yolov5/runs/detect/exp/image.jpg'): #assuming JPG\n",
        "    display(Image(filename=imageName))\n",
        "    print(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "7iiObB2WCMh6",
        "outputId": "09c6adb6-4938-4855-fd6e-ac89622a6fee"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_c6bf1b1d-1c67-4be8-9e25-d428e188a1bd\", \"best.pt\", 14330997)"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#export your model's weights for future use\n",
        "from google.colab import files\n",
        "files.download('./runs/train/exp/weights/best.pt')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install requests pillow"
      ],
      "metadata": {
        "id": "qosuEeoJcWP5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5349ffed-3bd6-4875-9615-27a61cc786d1"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (2.27.1)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (9.1.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests) (2021.5.30)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests) (1.26.6)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests) (2.10)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests) (2.0.12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "import cv2\n",
        "import requests\n",
        "from PIL import Image\n",
        "from requests_toolbelt.multipart.encoder import MultipartEncoder\n",
        "\n",
        "# Load Image with PIL\n",
        "img = cv2.imread(\"/content/image.jpg\")\n",
        "image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "pilImage = Image.fromarray(image)\n",
        "\n",
        "# Convert to JPEG Buffer\n",
        "buffered = io.BytesIO()\n",
        "pilImage.save(buffered, quality=100, format=\"JPEG\")\n",
        "\n",
        "# Build multipart form and post request\n",
        "m = MultipartEncoder(fields={'file': (\"imageToUpload\", buffered.getvalue(), \"image/jpeg\")})\n",
        "\n",
        "response = requests.post(\"https://detect.roboflow.com/license-plate-detection-x9ngg/1?api_key=cVEtmQca6JMsH0rWK9rV\", data=m, headers={'Content-Type': m.content_type})\n",
        "\n",
        "print(response)\n",
        "print(response.json())\n",
        "#cv2_imshow(img)"
      ],
      "metadata": {
        "id": "6xJz4Kc5cUIH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b735ed04-4e19-4d74-9eaa-33ca1981ec3e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<Response [200]>\n",
            "{'predictions': [{'x': 124.5, 'y': 79.5, 'width': 129, 'height': 49, 'class': 'License-Plate', 'confidence': 0.424}]}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "rNn-obvOGITm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa52f0d5-9a76-4373-becb-cbf2ae0682f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x= 124.5 y= 79.5 w= 129.0 h= 49.0\n"
          ]
        }
      ],
      "source": [
        "dic={}\n",
        "dic=response.json()\n",
        "out=dic['predictions']\n",
        "for i in out:\n",
        "  cls=i['class']\n",
        "  confidence=i['confidence']\n",
        "  x=float(i['x'])\n",
        "  y=float(i['y'])\n",
        "  w=float(i['width'])\n",
        "  h=float(i['height'])\n",
        "print('x=',x,'y=',y,'w=',w,'h=',h)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualization of license plate frame**"
      ],
      "metadata": {
        "id": "XHamrDR7JNmg"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "gYWUn_nFZLdR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "outputId": "ec184ae6-f51a-4026-db6e-d414844e832a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "Shape of the image (195, 259, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=259x195>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cropped Image\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=124x29>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "img = cv2.imread(\"/content/image.jpg\")\n",
        "print(type(img))\n",
        "# Shape of the image\n",
        "print(\"Shape of the image\", img.shape)\n",
        "# [rows, columns]\n",
        "crop = img[int(y)-10:int(y+h)-30, int(x)-50:int(x+w)-55]\n",
        "cv2_imshow(img)\n",
        "print('Cropped Image')\n",
        "cv2_imshow(crop)\n",
        "#display(Image(crop))\n",
        "cv2.waitKey(0)\n",
        "cv2.destroyAllWindows()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "mH2r558tZtkc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16bbcd55-94b5-4a0b-c97a-4d82a53133f2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29, 124, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "crop.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def zoom(img, zoom_factor=1.5):\n",
        "    return cv2.resize(img, None, fx=zoom_factor, fy=zoom_factor)\n",
        "zoomed = zoom(crop, 5)\n",
        "cv2.imwrite('zoomed.jpg',zoomed)\n",
        "cv2_imshow(zoomed)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "id": "WZ_PXEKK9f_w",
        "outputId": "bb65964f-4190-4ca5-a087-06fecc6c1ebf"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=620x145>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "files.download('zoomed.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "SwdcD6y5TzLu",
        "outputId": "c9203ab2-c320-4333-a23d-900a89b373e3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_57d2bfa9-7511-4217-8a25-5bcf13595f76\", \"zoomed.jpg\", 18203)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Real esrGAN for converting low resolution image to high resolution**"
      ],
      "metadata": {
        "id": "2_ZVmTO5Iotw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Clone Real-ESRGAN and enter the Real-ESRGAN\n",
        "!git clone https://github.com/xinntao/Real-ESRGAN.git\n",
        "%cd Real-ESRGAN\n",
        "# Set up the environment\n",
        "!pip install basicsr\n",
        "!pip install facexlib\n",
        "!pip install gfpgan\n",
        "!pip install -r requirements.txt\n",
        "!python setup.py develop\n",
        "# Download the pre-trained model\n",
        "!wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P experiments/pretrained_models"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "szCidYm0AwDB",
        "outputId": "16600d3f-d9ca-42ab-d69d-21c742dd3ee0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'Real-ESRGAN'...\n",
            "remote: Enumerating objects: 682, done.\u001b[K\n",
            "remote: Counting objects: 100% (17/17), done.\u001b[K\n",
            "remote: Compressing objects: 100% (15/15), done.\u001b[K\n",
            "remote: Total 682 (delta 4), reused 11 (delta 2), pack-reused 665\u001b[K\n",
            "Receiving objects: 100% (682/682), 5.03 MiB | 34.57 MiB/s, done.\n",
            "Resolving deltas: 100% (359/359), done.\n",
            "/content/yolov5/yolov5/Real-ESRGAN\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: basicsr in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from basicsr) (0.18.3)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from basicsr) (6.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from basicsr) (9.1.1)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from basicsr) (2.4.0)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from basicsr) (0.12.0+cu113)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from basicsr) (4.64.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from basicsr) (1.4.1)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from basicsr) (0.32.0)\n",
            "Requirement already satisfied: tb-nightly in /usr/local/lib/python3.7/dist-packages (from basicsr) (2.10.0a20220606)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from basicsr) (0.16.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from basicsr) (2.27.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from basicsr) (1.20.3)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.7/dist-packages (from basicsr) (1.11.0+cu113)\n",
            "Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from basicsr) (0.99)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from basicsr) (4.1.2.30)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7->basicsr) (4.2.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr) (2021.5.30)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr) (1.26.6)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr) (2021.11.2)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr) (2.4.1)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr) (2.6.3)\n",
            "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr) (3.2.2)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr) (1.3.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->basicsr) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->basicsr) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->basicsr) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->basicsr) (1.3.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib!=3.0.0,>=2.0.0->scikit-image->basicsr) (1.15.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (0.4.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (0.6.1)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (3.17.3)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (1.0.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (3.3.7)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (1.0.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (1.46.3)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (1.8.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (0.37.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (57.4.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr) (1.35.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr) (4.8)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr) (4.2.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tb-nightly->basicsr) (4.11.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tb-nightly->basicsr) (3.8.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tb-nightly->basicsr) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr) (3.2.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: facexlib in /usr/local/lib/python3.7/dist-packages (0.2.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from facexlib) (4.64.0)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from facexlib) (9.1.1)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from facexlib) (1.11.0+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from facexlib) (0.12.0+cu113)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from facexlib) (0.51.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from facexlib) (1.20.3)\n",
            "Requirement already satisfied: filterpy in /usr/local/lib/python3.7/dist-packages (from facexlib) (1.4.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from facexlib) (1.4.1)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from facexlib) (4.1.2.30)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from filterpy->facexlib) (3.2.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib) (2.8.2)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib) (1.3.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->filterpy->facexlib) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->facexlib) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->facexlib) (0.34.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch->facexlib) (4.2.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torchvision->facexlib) (2.27.1)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->facexlib) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->facexlib) (1.26.6)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->facexlib) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torchvision->facexlib) (2021.5.30)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gfpgan in /usr/local/lib/python3.7/dist-packages (1.3.2)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from gfpgan) (0.12.0+cu113)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.7/dist-packages (from gfpgan) (1.11.0+cu113)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from gfpgan) (6.0)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from gfpgan) (4.1.2.30)\n",
            "Requirement already satisfied: basicsr>=1.3.4.0 in /usr/local/lib/python3.7/dist-packages (from gfpgan) (1.3.5)\n",
            "Requirement already satisfied: numpy<1.21 in /usr/local/lib/python3.7/dist-packages (from gfpgan) (1.20.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gfpgan) (4.64.0)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from gfpgan) (0.32.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from gfpgan) (1.4.1)\n",
            "Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from gfpgan) (0.99)\n",
            "Requirement already satisfied: tb-nightly in /usr/local/lib/python3.7/dist-packages (from gfpgan) (2.10.0a20220606)\n",
            "Requirement already satisfied: facexlib>=0.2.0.3 in /usr/local/lib/python3.7/dist-packages (from gfpgan) (0.2.3)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.4.0->gfpgan) (0.18.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.4.0->gfpgan) (2.27.1)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.4.0->gfpgan) (9.1.1)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.4.0->gfpgan) (0.16.0)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.4.0->gfpgan) (2.4.0)\n",
            "Requirement already satisfied: filterpy in /usr/local/lib/python3.7/dist-packages (from facexlib>=0.2.0.3->gfpgan) (1.4.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from facexlib>=0.2.0.3->gfpgan) (0.51.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7->gfpgan) (4.2.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from filterpy->facexlib>=0.2.0.3->gfpgan) (3.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->gfpgan) (0.10.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->gfpgan) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->gfpgan) (2.4.7)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->gfpgan) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->filterpy->facexlib>=0.2.0.3->gfpgan) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->facexlib>=0.2.0.3->gfpgan) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->facexlib>=0.2.0.3->gfpgan) (0.34.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.4.0->gfpgan) (1.26.6)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.4.0->gfpgan) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.4.0->gfpgan) (2021.5.30)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.4.0->gfpgan) (2.0.12)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.4.0->gfpgan) (2.6.3)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.4.0->gfpgan) (1.3.0)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.4.0->gfpgan) (2021.11.2)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.4.0->gfpgan) (2.4.1)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (1.0.0)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (3.17.3)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (0.6.1)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (1.46.3)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (0.37.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (3.3.7)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (0.4.6)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (1.8.1)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->gfpgan) (1.0.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->gfpgan) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->gfpgan) (4.2.4)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->gfpgan) (4.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->gfpgan) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tb-nightly->gfpgan) (4.11.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tb-nightly->gfpgan) (3.8.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tb-nightly->gfpgan) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->gfpgan) (3.2.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: basicsr>=1.3.3.11 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 1)) (1.3.5)\n",
            "Requirement already satisfied: facexlib>=0.2.0.3 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 2)) (0.2.3)\n",
            "Requirement already satisfied: gfpgan>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 3)) (1.3.2)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (1.20.3)\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 5)) (4.1.2.30)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 6)) (9.1.1)\n",
            "Requirement already satisfied: torch>=1.7 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (1.11.0+cu113)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 8)) (0.12.0+cu113)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 9)) (4.64.0)\n",
            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.32.0)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.18.3)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.16.0)\n",
            "Requirement already satisfied: lmdb in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.99)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (6.0)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.4.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.27.1)\n",
            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.4.0)\n",
            "Requirement already satisfied: tb-nightly in /usr/local/lib/python3.7/dist-packages (from basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.10.0a20220606)\n",
            "Requirement already satisfied: filterpy in /usr/local/lib/python3.7/dist-packages (from facexlib>=0.2.0.3->-r requirements.txt (line 2)) (1.4.5)\n",
            "Requirement already satisfied: numba in /usr/local/lib/python3.7/dist-packages (from facexlib>=0.2.0.3->-r requirements.txt (line 2)) (0.51.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.7->-r requirements.txt (line 7)) (4.2.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (3.2.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from cycler>=0.10->matplotlib->filterpy->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from numba->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (57.4.0)\n",
            "Requirement already satisfied: llvmlite<0.35,>=0.34.0.dev0 in /usr/local/lib/python3.7/dist-packages (from numba->facexlib>=0.2.0.3->-r requirements.txt (line 2)) (0.34.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2021.5.30)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.26.6)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.7/dist-packages (from requests->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.0.12)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2021.11.2)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.6.3)\n",
            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (2.4.1)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from scikit-image->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.3.0)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.0.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.8.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.37.1)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.6.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.3.7)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.46.3)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.0.1)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.4.6)\n",
            "Requirement already satisfied: protobuf<3.20,>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.17.3)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.2.4)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.8)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (1.3.1)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (4.11.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.8.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->basicsr>=1.3.3.11->-r requirements.txt (line 1)) (3.2.0)\n",
            "running develop\n",
            "running egg_info\n",
            "creating realesrgan.egg-info\n",
            "writing realesrgan.egg-info/PKG-INFO\n",
            "writing dependency_links to realesrgan.egg-info/dependency_links.txt\n",
            "writing requirements to realesrgan.egg-info/requires.txt\n",
            "writing top-level names to realesrgan.egg-info/top_level.txt\n",
            "writing manifest file 'realesrgan.egg-info/SOURCES.txt'\n",
            "reading manifest template 'MANIFEST.in'\n",
            "adding license file 'LICENSE'\n",
            "writing manifest file 'realesrgan.egg-info/SOURCES.txt'\n",
            "running build_ext\n",
            "Creating /usr/local/lib/python3.7/dist-packages/realesrgan.egg-link (link to .)\n",
            "Removing realesrgan 0.2.5.0 from easy-install.pth file\n",
            "Adding realesrgan 0.2.5.0 to easy-install.pth file\n",
            "\n",
            "Installed /content/yolov5/yolov5/Real-ESRGAN\n",
            "Processing dependencies for realesrgan==0.2.5.0\n",
            "Searching for tqdm==4.64.0\n",
            "Best match: tqdm 4.64.0\n",
            "Adding tqdm 4.64.0 to easy-install.pth file\n",
            "Installing tqdm script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for torchvision==0.12.0+cu113\n",
            "Best match: torchvision 0.12.0+cu113\n",
            "Adding torchvision 0.12.0+cu113 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for torch==1.11.0+cu113\n",
            "Best match: torch 1.11.0+cu113\n",
            "Adding torch 1.11.0+cu113 to easy-install.pth file\n",
            "Installing convert-caffe2-to-onnx script to /usr/local/bin\n",
            "Installing convert-onnx-to-caffe2 script to /usr/local/bin\n",
            "Installing torchrun script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Pillow==9.1.1\n",
            "Best match: Pillow 9.1.1\n",
            "Adding Pillow 9.1.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for opencv-python==4.1.2.30\n",
            "Best match: opencv-python 4.1.2.30\n",
            "Adding opencv-python 4.1.2.30 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for numpy==1.20.3\n",
            "Best match: numpy 1.20.3\n",
            "Adding numpy 1.20.3 to easy-install.pth file\n",
            "Installing f2py script to /usr/local/bin\n",
            "Installing f2py3 script to /usr/local/bin\n",
            "Installing f2py3.7 script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for gfpgan==1.3.2\n",
            "Best match: gfpgan 1.3.2\n",
            "Adding gfpgan 1.3.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for facexlib==0.2.3\n",
            "Best match: facexlib 0.2.3\n",
            "Adding facexlib 0.2.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for basicsr==1.3.5\n",
            "Best match: basicsr 1.3.5\n",
            "Adding basicsr 1.3.5 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for requests==2.27.1\n",
            "Best match: requests 2.27.1\n",
            "Adding requests 2.27.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for typing-extensions==4.2.0\n",
            "Best match: typing-extensions 4.2.0\n",
            "Adding typing-extensions 4.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for yapf==0.32.0\n",
            "Best match: yapf 0.32.0\n",
            "Adding yapf 0.32.0 to easy-install.pth file\n",
            "Installing yapf script to /usr/local/bin\n",
            "Installing yapf-diff script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for PyYAML==6.0\n",
            "Best match: PyYAML 6.0\n",
            "Adding PyYAML 6.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tb-nightly==2.10.0a20220606\n",
            "Best match: tb-nightly 2.10.0a20220606\n",
            "Adding tb-nightly 2.10.0a20220606 to easy-install.pth file\n",
            "Installing tensorboard script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for scipy==1.4.1\n",
            "Best match: scipy 1.4.1\n",
            "Adding scipy 1.4.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for lmdb==0.99\n",
            "Best match: lmdb 0.99\n",
            "Adding lmdb 0.99 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for numba==0.51.2\n",
            "Best match: numba 0.51.2\n",
            "Adding numba 0.51.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for filterpy==1.4.5\n",
            "Best match: filterpy 1.4.5\n",
            "Adding filterpy 1.4.5 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for future==0.16.0\n",
            "Best match: future 0.16.0\n",
            "Adding future 0.16.0 to easy-install.pth file\n",
            "Installing futurize script to /usr/local/bin\n",
            "Installing pasteurize script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for scikit-image==0.18.3\n",
            "Best match: scikit-image 0.18.3\n",
            "Adding scikit-image 0.18.3 to easy-install.pth file\n",
            "Installing skivi script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for addict==2.4.0\n",
            "Best match: addict 2.4.0\n",
            "Adding addict 2.4.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for idna==2.10\n",
            "Best match: idna 2.10\n",
            "Adding idna 2.10 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for urllib3==1.26.6\n",
            "Best match: urllib3 1.26.6\n",
            "Adding urllib3 1.26.6 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for charset-normalizer==2.0.12\n",
            "Best match: charset-normalizer 2.0.12\n",
            "Adding charset-normalizer 2.0.12 to easy-install.pth file\n",
            "Installing normalizer script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for certifi==2021.5.30\n",
            "Best match: certifi 2021.5.30\n",
            "Adding certifi 2021.5.30 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for grpcio==1.46.3\n",
            "Best match: grpcio 1.46.3\n",
            "Adding grpcio 1.46.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for wheel==0.37.1\n",
            "Best match: wheel 0.37.1\n",
            "Adding wheel 0.37.1 to easy-install.pth file\n",
            "Installing wheel script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Markdown==3.3.7\n",
            "Best match: Markdown 3.3.7\n",
            "Adding Markdown 3.3.7 to easy-install.pth file\n",
            "Installing markdown_py script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for absl-py==1.0.0\n",
            "Best match: absl-py 1.0.0\n",
            "Adding absl-py 1.0.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for setuptools==57.4.0\n",
            "Best match: setuptools 57.4.0\n",
            "Adding setuptools 57.4.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for google-auth-oauthlib==0.4.6\n",
            "Best match: google-auth-oauthlib 0.4.6\n",
            "Adding google-auth-oauthlib 0.4.6 to easy-install.pth file\n",
            "Installing google-oauthlib-tool script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for Werkzeug==1.0.1\n",
            "Best match: Werkzeug 1.0.1\n",
            "Adding Werkzeug 1.0.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tensorboard-plugin-wit==1.8.1\n",
            "Best match: tensorboard-plugin-wit 1.8.1\n",
            "Adding tensorboard-plugin-wit 1.8.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for google-auth==1.35.0\n",
            "Best match: google-auth 1.35.0\n",
            "Adding google-auth 1.35.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for protobuf==3.17.3\n",
            "Best match: protobuf 3.17.3\n",
            "Adding protobuf 3.17.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tensorboard-data-server==0.6.1\n",
            "Best match: tensorboard-data-server 0.6.1\n",
            "Adding tensorboard-data-server 0.6.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for llvmlite==0.34.0\n",
            "Best match: llvmlite 0.34.0\n",
            "Adding llvmlite 0.34.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for matplotlib==3.2.2\n",
            "Best match: matplotlib 3.2.2\n",
            "Adding matplotlib 3.2.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for tifffile==2021.11.2\n",
            "Best match: tifffile 2021.11.2\n",
            "Adding tifffile 2021.11.2 to easy-install.pth file\n",
            "Installing lsm2bin script to /usr/local/bin\n",
            "Installing tiff2fsspec script to /usr/local/bin\n",
            "Installing tiffcomment script to /usr/local/bin\n",
            "Installing tifffile script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for imageio==2.4.1\n",
            "Best match: imageio 2.4.1\n",
            "Adding imageio 2.4.1 to easy-install.pth file\n",
            "Installing imageio_download_bin script to /usr/local/bin\n",
            "Installing imageio_remove_bin script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for networkx==2.6.3\n",
            "Best match: networkx 2.6.3\n",
            "Adding networkx 2.6.3 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for PyWavelets==1.3.0\n",
            "Best match: PyWavelets 1.3.0\n",
            "Adding PyWavelets 1.3.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for six==1.15.0\n",
            "Best match: six 1.15.0\n",
            "Adding six 1.15.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for importlib-metadata==4.11.4\n",
            "Best match: importlib-metadata 4.11.4\n",
            "Adding importlib-metadata 4.11.4 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for requests-oauthlib==1.3.1\n",
            "Best match: requests-oauthlib 1.3.1\n",
            "Adding requests-oauthlib 1.3.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyasn1-modules==0.2.8\n",
            "Best match: pyasn1-modules 0.2.8\n",
            "Adding pyasn1-modules 0.2.8 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for rsa==4.8\n",
            "Best match: rsa 4.8\n",
            "Adding rsa 4.8 to easy-install.pth file\n",
            "Installing pyrsa-decrypt script to /usr/local/bin\n",
            "Installing pyrsa-encrypt script to /usr/local/bin\n",
            "Installing pyrsa-keygen script to /usr/local/bin\n",
            "Installing pyrsa-priv2pub script to /usr/local/bin\n",
            "Installing pyrsa-sign script to /usr/local/bin\n",
            "Installing pyrsa-verify script to /usr/local/bin\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for cachetools==4.2.4\n",
            "Best match: cachetools 4.2.4\n",
            "Adding cachetools 4.2.4 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyparsing==2.4.7\n",
            "Best match: pyparsing 2.4.7\n",
            "Adding pyparsing 2.4.7 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for cycler==0.10.0\n",
            "Best match: cycler 0.10.0\n",
            "Adding cycler 0.10.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for python-dateutil==2.8.2\n",
            "Best match: python-dateutil 2.8.2\n",
            "Adding python-dateutil 2.8.2 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for kiwisolver==1.3.1\n",
            "Best match: kiwisolver 1.3.1\n",
            "Adding kiwisolver 1.3.1 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for zipp==3.8.0\n",
            "Best match: zipp 3.8.0\n",
            "Adding zipp 3.8.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for oauthlib==3.2.0\n",
            "Best match: oauthlib 3.2.0\n",
            "Adding oauthlib 3.2.0 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Searching for pyasn1==0.4.8\n",
            "Best match: pyasn1 0.4.8\n",
            "Adding pyasn1 0.4.8 to easy-install.pth file\n",
            "\n",
            "Using /usr/local/lib/python3.7/dist-packages\n",
            "Finished processing dependencies for realesrgan==0.2.5.0\n",
            "--2022-06-08 12:42:07--  https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth\n",
            "Resolving github.com (github.com)... 192.30.255.113\n",
            "Connecting to github.com (github.com)|192.30.255.113|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/387326890/08f0e941-ebb7-48f0-9d6a-73e87b710e7e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220608%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220608T124207Z&X-Amz-Expires=300&X-Amz-Signature=59df2dd1e723ff6b01d91b92e1b0ecbbab7a130b2f37423677fe08a93416ab6f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=387326890&response-content-disposition=attachment%3B%20filename%3DRealESRGAN_x4plus.pth&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-06-08 12:42:07--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/387326890/08f0e941-ebb7-48f0-9d6a-73e87b710e7e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220608%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220608T124207Z&X-Amz-Expires=300&X-Amz-Signature=59df2dd1e723ff6b01d91b92e1b0ecbbab7a130b2f37423677fe08a93416ab6f&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=387326890&response-content-disposition=attachment%3B%20filename%3DRealESRGAN_x4plus.pth&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 67040989 (64M) [application/octet-stream]\n",
            "Saving to: ‘experiments/pretrained_models/RealESRGAN_x4plus.pth’\n",
            "\n",
            "RealESRGAN_x4plus.p 100%[===================>]  63.93M  32.8MB/s    in 1.9s    \n",
            "\n",
            "2022-06-08 12:42:09 (32.8 MB/s) - ‘experiments/pretrained_models/RealESRGAN_x4plus.pth’ saved [67040989/67040989]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from google.colab import files\n",
        "import shutil\n",
        "\n",
        "upload_folder = 'upload'\n",
        "result_folder = 'results'\n",
        "\n",
        "if os.path.isdir(upload_folder):\n",
        "    shutil.rmtree(upload_folder)\n",
        "if os.path.isdir(result_folder):\n",
        "    shutil.rmtree(result_folder)\n",
        "os.mkdir(upload_folder)\n",
        "os.mkdir(result_folder)\n",
        "\n",
        "# upload images\n",
        "uploaded = files.upload()\n",
        "for filename in uploaded.keys():\n",
        "  dst_path = os.path.join(upload_folder, filename)\n",
        "  print(f'move {filename} to {dst_path}')\n",
        "  shutil.move(filename, dst_path)"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 91
        },
        "id": "-3VIhs3ZRFXX",
        "outputId": "0cfec5ca-e87d-41e5-a941-aefe6a6b739c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-070b5873-0578-44e1-8d2c-47940691993b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-070b5873-0578-44e1-8d2c-47940691993b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving zoomed.jpg to zoomed.jpg\n",
            "move zoomed.jpg to upload/zoomed.jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python inference_realesrgan.py -n RealESRGAN_x4plus -i upload --outscale 3.5 --face_enhance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bSL6t9fuUVL8",
        "outputId": "caa70bb5-1ce5-4962-de66-632d183cdbaa"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing 0 zoomed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# utils for visualization\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "def display(img1, img2):\n",
        "  fig = plt.figure(figsize=(25, 10))\n",
        "  ax1 = fig.add_subplot(1, 2, 1) \n",
        "  plt.title('Input image', fontsize=16)\n",
        "  ax1.axis('off')\n",
        "  ax2 = fig.add_subplot(1, 2, 2)\n",
        "  plt.title('Real-ESRGAN output', fontsize=16)\n",
        "  ax2.axis('off')\n",
        "  ax1.imshow(img1)\n",
        "  ax2.imshow(img2)\n",
        "def imread(img_path):\n",
        "  img = cv2.imread(img_path)\n",
        "  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
        "  return img\n",
        "\n",
        "# display each image in the upload folder\n",
        "import os\n",
        "import glob\n",
        "\n",
        "input_folder = 'upload'\n",
        "result_folder = 'results'\n",
        "input_list = sorted(glob.glob(os.path.join(input_folder, '*')))\n",
        "output_list = sorted(glob.glob(os.path.join(result_folder, '*')))\n",
        "for input_path, output_path in zip(input_list, output_list):\n",
        "  img_input = imread(input_path)\n",
        "  img_output = imread(output_path)\n",
        "  display(img_input, img_output)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        },
        "id": "zY8iPGSFUXFv",
        "outputId": "7d7dbbc7-5653-427e-cb09-b46957b56498"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cv2.imwrite('esroutput.jpg',img_output)\n",
        "files.download('esroutput.jpg')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "5rjObc7zUuhw",
        "outputId": "1617a45e-e3af-41be-8fa7-fd8fd12b7a21"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_18b1fe3f-bdf4-4eb1-aa6b-242963f6af55\", \"esroutput.jpg\", 227247)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install tesseract-ocr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Sp8W2ZGVmwj",
        "outputId": "5c558ec8-614f-4acc-d381-5b758d8788d6"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "tesseract-ocr is already the newest version (4.00~git2288-10f4998a-2).\n",
            "The following package was automatically installed and is no longer required:\n",
            "  libnvidia-common-460\n",
            "Use 'sudo apt autoremove' to remove it.\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **pytesseract for Optical Character Recognization**"
      ],
      "metadata": {
        "id": "IXxpGdT6I8ib"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pytesseract"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hRkAsfDKVpgI",
        "outputId": "6462a596-25de-480b-d66b-65dce1b50cf2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pytesseract in /usr/local/lib/python3.7/dist-packages (0.3.9)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.7/dist-packages (from pytesseract) (21.3)\n",
            "Requirement already satisfied: Pillow>=8.0.0 in /usr/local/lib/python3.7/dist-packages (from pytesseract) (9.1.1)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=21.3->pytesseract) (2.4.7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pytesseract\n",
        "import shutil\n",
        "import os\n",
        "import random\n",
        "try:\n",
        "    from PIL import Image\n",
        "except ImportError:\n",
        "    import Image\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "GtVGf9-dV2Fz",
        "outputId": "7c4ac409-dd8f-4e5b-cb64-bf725d9cacc3"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-adc1435b-89bd-43ad-bd21-64ca6cd90910\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-adc1435b-89bd-43ad-bd21-64ca6cd90910\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving esroutput.jpg to esroutput (1).jpg\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "extractedInformation = pytesseract.image_to_string(Image.open('esroutput.jpg'))"
      ],
      "metadata": {
        "id": "e_OAavxnWZIF"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(extractedInformation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7pKCrpu0WeNl",
        "outputId": "8380c0e0-95ff-41c7-e2c0-29f9dc97efc1"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KL 65H 4383\n",
            "\n",
            " \n",
            "\f\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Number plate detection",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}