Persian_License_Plate.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Persian License Plate Recognition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JNpGW-w8HH39"
      },
      "source": [
        "## Clone deep-text-recognition-benchmark repository"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VZ2WtpRLHA6j",
        "outputId": "20da7f4f-dd92-4545-949f-b78e3f650f82"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'deep-text-recognition-benchmark'...\n",
            "remote: Enumerating objects: 499, done.\u001b[K\n",
            "remote: Counting objects: 100% (4/4), done.\u001b[K\n",
            "remote: Compressing objects: 100% (4/4), done.\u001b[K\n",
            "remote: Total 499 (delta 0), reused 1 (delta 0), pack-reused 495\u001b[K\n",
            "Receiving objects: 100% (499/499), 3.07 MiB | 6.94 MiB/s, done.\n",
            "Resolving deltas: 100% (302/302), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/clovaai/deep-text-recognition-benchmark.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BjoZ7bpHON-"
      },
      "source": [
        "## Download The Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gq5CdZXIHKlY",
        "outputId": "62feda5e-a665-4c8a-a6a3-c070d1a5f8ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1ubkg7E2vGEOqS4K_quwf9Vl-i8IVpklM\n",
            "From (redirected): https://drive.google.com/uc?id=1ubkg7E2vGEOqS4K_quwf9Vl-i8IVpklM&confirm=t&uuid=a45a81da-4459-4ca6-b501-d1c798aff816\n",
            "To: /content/plate_img-train.zip\n",
            "100% 196M/196M [00:07<00:00, 26.6MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Train Dataset\n",
        "!gdown 1ubkg7E2vGEOqS4K_quwf9Vl-i8IVpklM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mmEmWmx_HP7r",
        "outputId": "f22f903f-a10a-4e1f-b760-6acef8820275"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1AL5Zsg2hDqcwF8ZmR0MJTbjgXIoE5W-I\n",
            "From (redirected): https://drive.google.com/uc?id=1AL5Zsg2hDqcwF8ZmR0MJTbjgXIoE5W-I&confirm=t&uuid=35b315c0-0ccd-4bba-b227-8a14fde3bc2f\n",
            "To: /content/plate_img-validation.zip\n",
            "100% 27.2M/27.2M [00:00<00:00, 29.7MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Validation Dataset\n",
        "!gdown 1AL5Zsg2hDqcwF8ZmR0MJTbjgXIoE5W-I"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_mYsXh7DHRY9",
        "outputId": "d6c33c96-2c42-420e-87ee-7590b95a6415"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1lLh_kxrHy1teUB2NguHVuOZwA5rjL5kx\n",
            "From (redirected): https://drive.google.com/uc?id=1lLh_kxrHy1teUB2NguHVuOZwA5rjL5kx&confirm=t&uuid=7cbfff4f-a819-4255-85ea-80a9c2c3f449\n",
            "To: /content/plate_img-test.zip\n",
            "100% 56.4M/56.4M [00:03<00:00, 16.4MB/s]\n"
          ]
        }
      ],
      "source": [
        "# Test Dataset\n",
        "!gdown 1lLh_kxrHy1teUB2NguHVuOZwA5rjL5kx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "triOaHG2HSqJ"
      },
      "outputs": [],
      "source": [
        "!unzip -qq ./plate_img-train.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ih-Wtsp4HUbC"
      },
      "outputs": [],
      "source": [
        "!unzip -qq ./plate_img-test.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "y0bvqkLTHVgp"
      },
      "outputs": [],
      "source": [
        "!unzip -qq ./plate_img-validation.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X6j8WYScHYel"
      },
      "source": [
        "## Prepare The Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hFU6RLy_HWo8"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from lxml import etree as et\n",
        "from lxml.etree import _Element\n",
        "from tqdm import tqdm\n",
        "from prepare_dataset import prepare_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ABpBtzA6HaEj"
      },
      "outputs": [],
      "source": [
        "words_dict: dict[str, str] = {\n",
        "    'A': 'الف',\n",
        "    'B': 'ب',\n",
        "    'P': 'پ',\n",
        "    'T': 'ت',\n",
        "    'Y': 'ث',\n",
        "    'Z': 'ز',\n",
        "    'X': 'ش',\n",
        "    'E': 'ع',\n",
        "    'F': 'ف',\n",
        "    'K': 'ک',\n",
        "    'G': 'گ',\n",
        "    'D': 'D',\n",
        "    'S': 'S',\n",
        "    'J': 'ج',\n",
        "    'W': 'د',\n",
        "    'C': 'س',\n",
        "    'U': 'ص',\n",
        "    'R': 'ط',\n",
        "    'Q': 'ق',\n",
        "    'L': 'ل',\n",
        "    'M': 'م',\n",
        "    'N': 'ن',\n",
        "    'V': 'و',\n",
        "    'H': 'ه',\n",
        "    'I': 'ی',\n",
        "    '0': '0',\n",
        "    '1': '1',\n",
        "    '2': '2',\n",
        "    '3': '3',\n",
        "    '4': '4',\n",
        "    '5': '5',\n",
        "    '6': '6',\n",
        "    '7': '7',\n",
        "    '8': '8',\n",
        "    '9': '9',\n",
        "    '@': 'ویلچر',\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCvUM4D8HcVu",
        "outputId": "2fbc30e7-596c-4887-9711-5d85b6b9ccf0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 38762/38762 [00:01<00:00, 21275.81it/s]\n"
          ]
        }
      ],
      "source": [
        "prepare_dataset('/content/train', 'train')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zslmZhY_HeBr",
        "outputId": "98651997-21df-414e-81e9-d920a87b84ff"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 5610/5610 [00:00<00:00, 21103.80it/s]\n"
          ]
        }
      ],
      "source": [
        "prepare_dataset('/content/validation', 'validation')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wzK6C61ZHfPV",
        "outputId": "87d31fce-4256-45ee-9328-582354b9a679"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 11118/11118 [00:00<00:00, 11616.85it/s]\n"
          ]
        }
      ],
      "source": [
        "prepare_dataset('/content/test', 'test')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_1lLWDO9Hgzz"
      },
      "outputs": [],
      "source": [
        "!pip -q install lmdb fire"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kO8HRZvpHihr",
        "outputId": "4ca2debc-8fac-41a9-9d09-a2ec69fe7f15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Written 1000 / 19381\n",
            "Written 2000 / 19381\n",
            "Written 3000 / 19381\n",
            "Written 4000 / 19381\n",
            "Written 5000 / 19381\n",
            "Written 6000 / 19381\n",
            "Written 7000 / 19381\n",
            "Written 8000 / 19381\n",
            "Written 9000 / 19381\n",
            "Written 10000 / 19381\n",
            "Written 11000 / 19381\n",
            "Written 12000 / 19381\n",
            "Written 13000 / 19381\n",
            "Written 14000 / 19381\n",
            "Written 15000 / 19381\n",
            "Written 16000 / 19381\n",
            "Written 17000 / 19381\n",
            "Written 18000 / 19381\n",
            "Written 19000 / 19381\n",
            "Created dataset with 19381 samples\n"
          ]
        }
      ],
      "source": [
        "!python3 /content/deep-text-recognition-benchmark/create_lmdb_dataset.py \\\n",
        "--inputPath /content/ --gtFile /content/gt_train.txt --outputPath /content/train_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTODpS1OHjxH",
        "outputId": "a4a59198-6f26-4956-c49b-575f2db6bd36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Written 1000 / 2805\n",
            "Written 2000 / 2805\n",
            "Created dataset with 2805 samples\n"
          ]
        }
      ],
      "source": [
        "!python3 /content/deep-text-recognition-benchmark/create_lmdb_dataset.py \\\n",
        "--inputPath /content/ --gtFile /content/gt_validation.txt \\\n",
        "--outputPath /content/validation_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_s15rgZDHlC1",
        "outputId": "a53edb34-a3ff-4faa-e4ec-42e85c717b9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Written 1000 / 5559\n",
            "Written 2000 / 5559\n",
            "Written 3000 / 5559\n",
            "Written 4000 / 5559\n",
            "Written 5000 / 5559\n",
            "Created dataset with 5559 samples\n"
          ]
        }
      ],
      "source": [
        "!python3 /content/deep-text-recognition-benchmark/create_lmdb_dataset.py \\\n",
        "--inputPath /content/ --gtFile /content/gt_test.txt --outputPath /content/test_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7mxDjWp-Hp4G"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4Ze1CYfHqOX",
        "outputId": "d7068b2b-8505-4d91-9169-b8d49a891bda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Filtering the images containing characters which are not in opt.character\n",
            "Filtering the images whose label is longer than opt.batch_max_length\n",
            "--------------------------------------------------------------------------------\n",
            "dataset_root: /content/train_dataset\n",
            "opt.select_data: ['/']\n",
            "opt.batch_ratio: ['1']\n",
            "--------------------------------------------------------------------------------\n",
            "dataset_root:    /content/train_dataset\t dataset: /\n",
            "sub-directory:\t/.\t num samples: 19311\n",
            "num total samples of /: 19311 x 1.0 (total_data_usage_ratio) = 19311\n",
            "num samples of / per batch: 192 x 1.0 (batch_ratio) = 192\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "--------------------------------------------------------------------------------\n",
            "Total_batch_size: 192 = 192\n",
            "--------------------------------------------------------------------------------\n",
            "dataset_root:    /content/validation_dataset\t dataset: /\n",
            "sub-directory:\t/.\t num samples: 2793\n",
            "--------------------------------------------------------------------------------\n",
            "model input parameters 32 100 20 1 512 256 38 8 TPS ResNet BiLSTM Attn\n",
            "Skip Transformation.LocalizationNetwork.localization_fc2.weight as it is already initialized\n",
            "Skip Transformation.LocalizationNetwork.localization_fc2.bias as it is already initialized\n",
            "Model:\n",
            "DataParallel(\n",
            "  (module): Model(\n",
            "    (Transformation): TPS_SpatialTransformerNetwork(\n",
            "      (LocalizationNetwork): LocalizationNetwork(\n",
            "        (conv): Sequential(\n",
            "          (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (2): ReLU(inplace=True)\n",
            "          (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "          (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (5): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (6): ReLU(inplace=True)\n",
            "          (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "          (8): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (10): ReLU(inplace=True)\n",
            "          (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "          (12): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "          (13): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "          (14): ReLU(inplace=True)\n",
            "          (15): AdaptiveAvgPool2d(output_size=1)\n",
            "        )\n",
            "        (localization_fc1): Sequential(\n",
            "          (0): Linear(in_features=512, out_features=256, bias=True)\n",
            "          (1): ReLU(inplace=True)\n",
            "        )\n",
            "        (localization_fc2): Linear(in_features=256, out_features=40, bias=True)\n",
            "      )\n",
            "      (GridGenerator): GridGenerator()\n",
            "    )\n",
            "    (FeatureExtraction): ResNet_FeatureExtractor(\n",
            "      (ConvNet): ResNet(\n",
            "        (conv0_1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn0_1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (conv0_2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn0_2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (relu): ReLU(inplace=True)\n",
            "        (maxpool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "        (layer1): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "        )\n",
            "        (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (maxpool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
            "        (layer2): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (maxpool3): MaxPool2d(kernel_size=2, stride=(2, 1), padding=(0, 1), dilation=1, ceil_mode=False)\n",
            "        (layer3): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "            (downsample): Sequential(\n",
            "              (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
            "              (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            )\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (3): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (4): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (conv3): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (layer4): Sequential(\n",
            "          (0): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (1): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "          (2): BasicBlock(\n",
            "            (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
            "            (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "            (relu): ReLU(inplace=True)\n",
            "          )\n",
            "        )\n",
            "        (conv4_1): Conv2d(512, 512, kernel_size=(2, 2), stride=(2, 1), padding=(0, 1), bias=False)\n",
            "        (bn4_1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "        (conv4_2): Conv2d(512, 512, kernel_size=(2, 2), stride=(1, 1), bias=False)\n",
            "        (bn4_2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
            "      )\n",
            "    )\n",
            "    (AdaptiveAvgPool): AdaptiveAvgPool2d(output_size=(None, 1))\n",
            "    (SequenceModeling): Sequential(\n",
            "      (0): BidirectionalLSTM(\n",
            "        (rnn): LSTM(512, 256, batch_first=True, bidirectional=True)\n",
            "        (linear): Linear(in_features=512, out_features=256, bias=True)\n",
            "      )\n",
            "      (1): BidirectionalLSTM(\n",
            "        (rnn): LSTM(256, 256, batch_first=True, bidirectional=True)\n",
            "        (linear): Linear(in_features=512, out_features=256, bias=True)\n",
            "      )\n",
            "    )\n",
            "    (Prediction): Attention(\n",
            "      (attention_cell): AttentionCell(\n",
            "        (i2h): Linear(in_features=256, out_features=256, bias=False)\n",
            "        (h2h): Linear(in_features=256, out_features=256, bias=True)\n",
            "        (score): Linear(in_features=256, out_features=1, bias=False)\n",
            "        (rnn): LSTMCell(294, 256)\n",
            "      )\n",
            "      (generator): Linear(in_features=256, out_features=38, bias=True)\n",
            "    )\n",
            "  )\n",
            ")\n",
            "Trainable params num :  49555182\n",
            "Optimizer:\n",
            "Adadelta (\n",
            "Parameter Group 0\n",
            "    differentiable: False\n",
            "    eps: 1e-08\n",
            "    foreach: None\n",
            "    lr: 1\n",
            "    maximize: False\n",
            "    rho: 0.95\n",
            "    weight_decay: 0\n",
            ")\n",
            "------------ Options -------------\n",
            "exp_name: TPS-ResNet-BiLSTM-Attn-Seed1111\n",
            "train_data: /content/train_dataset\n",
            "valid_data: /content/validation_dataset\n",
            "manualSeed: 1111\n",
            "workers: 4\n",
            "batch_size: 192\n",
            "num_iter: 9000\n",
            "valInterval: 200\n",
            "saved_model: \n",
            "FT: False\n",
            "adam: False\n",
            "lr: 1\n",
            "beta1: 0.9\n",
            "rho: 0.95\n",
            "eps: 1e-08\n",
            "grad_clip: 5\n",
            "baiduCTC: False\n",
            "select_data: ['/']\n",
            "batch_ratio: ['1']\n",
            "total_data_usage_ratio: 1.0\n",
            "batch_max_length: 8\n",
            "imgH: 32\n",
            "imgW: 100\n",
            "rgb: False\n",
            "character: 0123456789abcdefghijklmnopqrstuvwxyz\n",
            "sensitive: False\n",
            "PAD: False\n",
            "data_filtering_off: False\n",
            "Transformation: TPS\n",
            "FeatureExtraction: ResNet\n",
            "SequenceModeling: BiLSTM\n",
            "Prediction: Attn\n",
            "num_fiducial: 20\n",
            "input_channel: 1\n",
            "output_channel: 512\n",
            "hidden_size: 256\n",
            "num_gpu: 1\n",
            "num_class: 38\n",
            "---------------------------------------\n",
            "\n",
            "[1/9000] Train loss: 3.65451, Valid loss: 3.57280, Elapsed_time: 14.07718\n",
            "Current_accuracy : 0.000, Current_norm_ED  : 0.02\n",
            "Best_accuracy    : 0.000, Best_norm_ED     : 0.02\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "85u27834                  | 2222222k                  | 0.0000\tFalse\n",
            "94e54533                  | kkkkkkkk                  | 0.0000\tFalse\n",
            "41r24477                  | kkkkkkkk                  | 0.0000\tFalse\n",
            "8251242                   | kkkkkkkk                  | 0.0000\tFalse\n",
            "61w36121                  | kkkkkkkk                  | 0.0000\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[200/9000] Train loss: 2.44050, Valid loss: 2.21856, Elapsed_time: 193.19139\n",
            "Current_accuracy : 0.000, Current_norm_ED  : 0.16\n",
            "Best_accuracy    : 0.000, Best_norm_ED     : 0.16\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "91u17734                  | 27w7511                   | 0.0000\tFalse\n",
            "29t41211                  | 68u7711                   | 0.0000\tFalse\n",
            "37b28120                  | 28u7711                   | 0.0000\tFalse\n",
            "48m73619                  | 26w7711                   | 0.0000\tFalse\n",
            "27v74955                  | 22u7711                   | 0.0000\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[400/9000] Train loss: 2.07666, Valid loss: 1.87142, Elapsed_time: 365.56140\n",
            "Current_accuracy : 0.000, Current_norm_ED  : 0.30\n",
            "Best_accuracy    : 0.000, Best_norm_ED     : 0.30\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "39c36818                  | 33b34210                  | 0.0000\tFalse\n",
            "18q41129                  | 55w11111                  | 0.0000\tFalse\n",
            "51l11263                  | 55w11111                  | 0.0001\tFalse\n",
            "89l84411                  | 55r8881                   | 0.0000\tFalse\n",
            "82q62277                  | 55u33233                  | 0.0001\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[600/9000] Train loss: 1.17496, Valid loss: 1.07547, Elapsed_time: 539.85940\n",
            "Current_accuracy : 3.294, Current_norm_ED  : 0.68\n",
            "Best_accuracy    : 3.294, Best_norm_ED     : 0.68\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "73u59418                  | 73u5418                   | 0.0512\tFalse\n",
            "36l66944                  | 36v69944                  | 0.0309\tFalse\n",
            "24w86266                  | 24v86666                  | 0.0165\tFalse\n",
            "43w19571                  | 4391571                   | 0.0240\tFalse\n",
            "55u21536                  | 55c21566                  | 0.0335\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[800/9000] Train loss: 0.50032, Valid loss: 0.39463, Elapsed_time: 715.23426\n",
            "Current_accuracy : 69.352, Current_norm_ED  : 0.91\n",
            "Best_accuracy    : 69.352, Best_norm_ED     : 0.91\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "58m55410                  | 58m55410                  | 0.6456\tTrue\n",
            "98w78299                  | 98w78299                  | 0.0593\tTrue\n",
            "22c82916                  | 22u82916                  | 0.5470\tFalse\n",
            "1868399                   | 1868399                   | 0.6794\tTrue\n",
            "88q66912                  | 88q66912                  | 0.5789\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[1000/9000] Train loss: 0.30336, Valid loss: 0.34903, Elapsed_time: 889.20775\n",
            "Current_accuracy : 79.556, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "55i96188                  | 55i96188                  | 0.8487\tTrue\n",
            "74l55388                  | 74l55388                  | 0.7028\tTrue\n",
            "79v92777                  | 79v92777                  | 0.8820\tTrue\n",
            "14l33644                  | 14l33644                  | 0.6802\tTrue\n",
            "49v26611                  | 49v26611                  | 0.8178\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[1200/9000] Train loss: 0.24624, Valid loss: 0.36751, Elapsed_time: 1063.01031\n",
            "Current_accuracy : 71.858, Current_norm_ED  : 0.91\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "22u22668                  | 22u22668                  | 0.8445\tTrue\n",
            "44i61615                  | 44i61615                  | 0.4189\tTrue\n",
            "73u14610                  | 73u14610                  | 0.7766\tTrue\n",
            "84c87511                  | 84c87511                  | 0.6509\tTrue\n",
            "89m72955                  | 89m72955                  | 0.7716\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[1400/9000] Train loss: 0.21505, Valid loss: 0.33723, Elapsed_time: 1236.37087\n",
            "Current_accuracy : 76.262, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "52w24314                  | 52v7411                   | 0.0903\tFalse\n",
            "73l76599                  | 73l76599                  | 0.8800\tTrue\n",
            "47q95729                  | 47q95729                  | 0.9095\tTrue\n",
            "43n82688                  | 43n82688                  | 0.9058\tTrue\n",
            "75m86216                  | 75m86216                  | 0.9106\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[1600/9000] Train loss: 0.17297, Valid loss: 0.40977, Elapsed_time: 1409.41178\n",
            "Current_accuracy : 71.894, Current_norm_ED  : 0.92\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "64i81299                  | 64i81299                  | 0.7050\tTrue\n",
            "29b56438                  | 29b56438                  | 0.8614\tTrue\n",
            "98v95544                  | 98v95544                  | 0.3988\tTrue\n",
            "6l93388                   | 68833888                  | 0.0716\tFalse\n",
            "12l26511                  | 12l26511                  | 0.7931\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[1800/9000] Train loss: 0.14137, Valid loss: 0.35298, Elapsed_time: 1584.16833\n",
            "Current_accuracy : 78.339, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "17j69453                  | 17j69453                  | 0.8374\tTrue\n",
            "38b29699                  | 38b29699                  | 0.8020\tTrue\n",
            "776855                    | 7678355                   | 0.6711\tFalse\n",
            "14u98710                  | 14u98710                  | 0.7705\tTrue\n",
            "26m19216                  | 26m19216                  | 0.8927\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[2000/9000] Train loss: 0.12301, Valid loss: 0.36312, Elapsed_time: 1759.22428\n",
            "Current_accuracy : 78.410, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "56u41414                  | 56u41414                  | 0.9539\tTrue\n",
            "69b63641                  | 69b63641                  | 0.6928\tTrue\n",
            "38n69511                  | 38n69511                  | 0.9105\tTrue\n",
            "49q97466                  | 49n97466                  | 0.8794\tFalse\n",
            "75v69866                  | 75v69866                  | 0.9602\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[2200/9000] Train loss: 0.10874, Valid loss: 0.37081, Elapsed_time: 1933.79159\n",
            "Current_accuracy : 79.270, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "97m75743                  | 97m75743                  | 0.9677\tTrue\n",
            "19r31816                  | 19r31816                  | 0.9540\tTrue\n",
            "84q36916                  | 84q36916                  | 0.7183\tTrue\n",
            "49i744                    | 53926624                  | 0.0486\tFalse\n",
            "38q74388                  | 28q74388                  | 0.7523\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[2400/9000] Train loss: 0.08739, Valid loss: 0.37040, Elapsed_time: 2106.56798\n",
            "Current_accuracy : 76.799, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "12                        | 82313                     | 0.0851\tFalse\n",
            "45i64919                  | 45i64999                  | 0.4740\tFalse\n",
            "76j59977                  | 76j59977                  | 0.9362\tTrue\n",
            "45q33829                  | 45q33829                  | 0.9650\tTrue\n",
            "28m59398                  | 28m59398                  | 0.9772\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[2600/9000] Train loss: 0.06841, Valid loss: 0.42897, Elapsed_time: 2277.75393\n",
            "Current_accuracy : 78.195, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "49v75444                  | 49v75444                  | 0.3478\tTrue\n",
            "33u88422                  | 33u88422                  | 0.9863\tTrue\n",
            "22r97668                  | 22r97668                  | 0.9780\tTrue\n",
            "26w76116                  | 26w76116                  | 0.9915\tTrue\n",
            "4m25510                   | 42m25510                  | 0.9411\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[2800/9000] Train loss: 0.07041, Valid loss: 0.44364, Elapsed_time: 2450.80350\n",
            "Current_accuracy : 75.689, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "63q38574                  | 63q38574                  | 0.9521\tTrue\n",
            "57u34834                  | 57u34834                  | 0.6201\tTrue\n",
            "53i28266                  | 53i28266                  | 0.5106\tTrue\n",
            "94w72924                  | 94w72924                  | 0.9271\tTrue\n",
            "89w29587                  | 89w29587                  | 0.9451\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[3000/9000] Train loss: 0.05170, Valid loss: 0.42632, Elapsed_time: 2624.78875\n",
            "Current_accuracy : 78.160, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "52q96888                  | 52q96888                  | 0.9902\tTrue\n",
            "69i87991                  | 69i87991                  | 0.9861\tTrue\n",
            "75v76868                  | 75v76868                  | 0.9808\tTrue\n",
            "31t46418                  | 31t46418                  | 0.9899\tTrue\n",
            "54r27655                  | 54r27655                  | 0.9903\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[3200/9000] Train loss: 0.05030, Valid loss: 0.43885, Elapsed_time: 2798.51219\n",
            "Current_accuracy : 76.047, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.556, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "49q56129                  | 49q56129                  | 0.7021\tTrue\n",
            "81w44474                  | 81w44474                  | 0.9900\tTrue\n",
            "45j11798                  | 45j11798                  | 0.9945\tTrue\n",
            "42q38866                  | 42q28866                  | 0.5779\tFalse\n",
            "99u83455                  | 99u83455                  | 0.9927\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[3400/9000] Train loss: 0.03840, Valid loss: 0.45012, Elapsed_time: 2970.04954\n",
            "Current_accuracy : 79.842, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 79.842, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "41c48640                  | 41u48622                  | 0.3605\tFalse\n",
            "55q25813                  | 55q25813                  | 0.9974\tTrue\n",
            "69r22844                  | 69r22844                  | 0.9920\tTrue\n",
            "75t15922                  | 75t15922                  | 0.9538\tTrue\n",
            "34n28611                  | 34n28611                  | 0.9923\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[3600/9000] Train loss: 0.03989, Valid loss: 0.45230, Elapsed_time: 3143.08508\n",
            "Current_accuracy : 76.763, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.842, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "52u42844                  | 52u42844                  | 0.8353\tTrue\n",
            "67q98942                  | 67q98942                  | 0.9917\tTrue\n",
            "8433722                   | 8433722                   | 0.9938\tTrue\n",
            "81q51116                  | 81q51116                  | 0.9954\tTrue\n",
            "81q16777                  | 81q16777                  | 0.9878\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[3800/9000] Train loss: 0.02543, Valid loss: 0.48415, Elapsed_time: 3316.03156\n",
            "Current_accuracy : 75.725, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.842, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "65j89421                  | 65j89421                  | 0.9753\tTrue\n",
            "98n83611                  | 98n83611                  | 0.4107\tTrue\n",
            "38l21833                  | 38l21833                  | 0.9888\tTrue\n",
            "6w521                     | ww11                      | 0.7602\tFalse\n",
            "14w19713                  | 14w19713                  | 0.9670\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[4000/9000] Train loss: 0.02881, Valid loss: 0.45812, Elapsed_time: 3489.19389\n",
            "Current_accuracy : 78.052, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 79.842, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "82b73618                  | 82b73618                  | 0.9871\tTrue\n",
            "27n87744                  | 27n87744                  | 0.8429\tTrue\n",
            "49n87216                  | 49n87216                  | 0.9944\tTrue\n",
            "5956133                   | 5956133                   | 0.8896\tTrue\n",
            "47i13178                  | 47i13178                  | 0.9926\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[4200/9000] Train loss: 0.01769, Valid loss: 0.48861, Elapsed_time: 3659.72839\n",
            "Current_accuracy : 79.234, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 79.842, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "83r46211                  | 83r46211                  | 0.9977\tTrue\n",
            "69r31677                  | 69r31677                  | 0.9976\tTrue\n",
            "37v46634                  | 37v46634                  | 0.9967\tTrue\n",
            "86m54974                  | 86m54974                  | 0.9984\tTrue\n",
            "18i87311                  | 18i87311                  | 0.9986\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[4400/9000] Train loss: 0.02112, Valid loss: 0.51273, Elapsed_time: 3830.33820\n",
            "Current_accuracy : 80.344, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "25v85610                  | 25v85610                  | 0.9922\tTrue\n",
            "57w25938                  | 57w25938                  | 0.9986\tTrue\n",
            "37t91922                  | 37t91922                  | 0.9977\tTrue\n",
            "92c58966                  | 92c58966                  | 0.9951\tTrue\n",
            "67i95110                  | 67i95110                  | 0.9985\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[4600/9000] Train loss: 0.02355, Valid loss: 0.51831, Elapsed_time: 4007.73988\n",
            "Current_accuracy : 79.126, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "85q28853                  | 85q28853                  | 0.9977\tTrue\n",
            "56w67453                  | 56w67453                  | 0.9876\tTrue\n",
            "91n12344                  | 91n12344                  | 0.9118\tTrue\n",
            "992n1844                  | 92n18944                  | 0.9931\tFalse\n",
            "7i75                      | 7t75                      | 0.6733\tFalse\n",
            "--------------------------------------------------------------------------------\n",
            "[4800/9000] Train loss: 0.01644, Valid loss: 0.50975, Elapsed_time: 4179.77424\n",
            "Current_accuracy : 79.950, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "26l92688                  | 26l9461                   | 0.4275\tFalse\n",
            "99i58877                  | 99i58877                  | 0.9827\tTrue\n",
            "59l72416                  | 59l72416                  | 0.9994\tTrue\n",
            "79u99544                  | 79u99544                  | 0.9984\tTrue\n",
            "35l43969                  | 35l43969                  | 0.9989\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[5000/9000] Train loss: 0.00779, Valid loss: 0.56822, Elapsed_time: 4352.15390\n",
            "Current_accuracy : 75.797, Current_norm_ED  : 0.93\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "75j94298                  | 75j94298                  | 0.8672\tTrue\n",
            "19v88221                  | 14v88221                  | 0.3794\tFalse\n",
            "55u13834                  | 55u13844                  | 0.2892\tFalse\n",
            "72t66568                  | 72t66568                  | 0.9900\tTrue\n",
            "61n39788                  | 61n39788                  | 0.9098\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[5200/9000] Train loss: 0.02097, Valid loss: 0.48735, Elapsed_time: 4524.27953\n",
            "Current_accuracy : 78.554, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "49i21466                  | 49i21466                  | 0.9692\tTrue\n",
            "57u34834                  | 57u34834                  | 0.9983\tTrue\n",
            "47u85716                  | 47u85716                  | 0.9963\tTrue\n",
            "39w18716                  | 39w18716                  | 0.9950\tTrue\n",
            "13r36918                  | 13r36918                  | 0.9953\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[5400/9000] Train loss: 0.02235, Valid loss: 0.51681, Elapsed_time: 4695.83930\n",
            "Current_accuracy : 78.912, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "53m85916                  | 53m85916                  | 0.9977\tTrue\n",
            "54b23377                  | 54t3337                   | 0.5009\tFalse\n",
            "75l55788                  | 75l55788                  | 0.9937\tTrue\n",
            "71q56116                  | 71q56116                  | 0.9996\tTrue\n",
            "45j93716                  | 45j93716                  | 0.9970\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[5600/9000] Train loss: 0.01166, Valid loss: 0.56427, Elapsed_time: 4869.04839\n",
            "Current_accuracy : 79.986, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "22q28226                  | 22q38326                  | 0.9723\tFalse\n",
            "45q33829                  | 45q33829                  | 0.9991\tTrue\n",
            "824333                    | 8234333                   | 0.9667\tFalse\n",
            "82n82212                  | 82n82212                  | 0.9991\tTrue\n",
            "1274811                   | 1274811                   | 0.9994\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[5800/9000] Train loss: 0.00376, Valid loss: 0.56765, Elapsed_time: 5043.10838\n",
            "Current_accuracy : 80.201, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "72v16824                  | 72v16824                  | 0.9974\tTrue\n",
            "52i34710                  | 52i44710                  | 0.5373\tFalse\n",
            "5v85610                   | 35v85610                  | 0.2443\tFalse\n",
            "24e67955                  | 4e67955                   | 0.9758\tFalse\n",
            "47v15988                  | 47v15988                  | 0.9993\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[6000/9000] Train loss: 0.00174, Valid loss: 0.59555, Elapsed_time: 5217.49023\n",
            "Current_accuracy : 80.057, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "46r56316                  | 46r56316                  | 0.9996\tTrue\n",
            "27i94822                  | 27i94822                  | 0.9991\tTrue\n",
            "75i31866                  | 75i31866                  | 0.9994\tTrue\n",
            "28m78498                  | 28m78498                  | 0.9997\tTrue\n",
            "51l49277                  | 51l49277                  | 0.9982\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[6200/9000] Train loss: 0.00113, Valid loss: 0.60644, Elapsed_time: 5390.51468\n",
            "Current_accuracy : 80.057, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "64u32934                  | 64u32934                  | 0.9997\tTrue\n",
            "13l42977                  | 13l42977                  | 0.9812\tTrue\n",
            "b75484                    | 49b5448                   | 0.9242\tFalse\n",
            "34w45910                  | 34w45910                  | 0.9991\tTrue\n",
            "41n42368                  | 41n42368                  | 0.9982\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[6400/9000] Train loss: 0.00115, Valid loss: 0.62415, Elapsed_time: 5563.24749\n",
            "Current_accuracy : 80.165, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.344, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "14w13491                  | 14w13491                  | 0.9999\tTrue\n",
            "866210                    | 866210                    | 0.7364\tTrue\n",
            "55w97419                  | 55w97419                  | 0.7940\tTrue\n",
            "12m14255                  | 12m14255                  | 0.9997\tTrue\n",
            "26l53141                  | 26l53141                  | 0.9994\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[6600/9000] Train loss: 0.00096, Valid loss: 0.62970, Elapsed_time: 5736.63990\n",
            "Current_accuracy : 80.415, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "83b91118                  | 83b91118                  | 0.9998\tTrue\n",
            "21m79898                  | 21m79898                  | 0.9995\tTrue\n",
            "69i7688                   | 9b9                       | 0.3332\tFalse\n",
            "81b39874                  | 81b39874                  | 0.8598\tTrue\n",
            "59m63911                  | 59m63911                  | 0.9990\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[6800/9000] Train loss: 0.00086, Valid loss: 0.65298, Elapsed_time: 5909.87119\n",
            "Current_accuracy : 80.380, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "41c26366                  | 41c26366                  | 0.9977\tTrue\n",
            "47j47999                  | 47j47999                  | 0.9988\tTrue\n",
            "14r88216                  | 14r8816                   | 0.9019\tFalse\n",
            "78l18144                  | 78l18144                  | 0.9997\tTrue\n",
            "52i34710                  | 52i34710                  | 0.6598\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[7000/9000] Train loss: 0.00088, Valid loss: 0.65052, Elapsed_time: 6083.08060\n",
            "Current_accuracy : 80.344, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "36r7598                   | 36r75968                  | 0.9972\tFalse\n",
            "555j1716                  | 55j51716                  | 0.9997\tFalse\n",
            "91w77453                  | 91w77453                  | 0.9995\tTrue\n",
            "45m41963                  | 45m41963                  | 0.9997\tTrue\n",
            "35m72211                  | 35m72211                  | 0.9998\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[7200/9000] Train loss: 0.00068, Valid loss: 0.65867, Elapsed_time: 6255.61663\n",
            "Current_accuracy : 80.201, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "97u46934                  | 97u46934                  | 0.9997\tTrue\n",
            "78r17693                  | 78r17693                  | 0.9998\tTrue\n",
            "72n32877                  | 72n32877                  | 0.9995\tTrue\n",
            "59n95636                  | 59n95636                  | 0.9999\tTrue\n",
            "42q69828                  | 42q69828                  | 0.9998\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[7400/9000] Train loss: 0.00072, Valid loss: 0.67267, Elapsed_time: 6429.15769\n",
            "Current_accuracy : 80.380, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "81l31310                  | 81l31310                  | 0.9958\tTrue\n",
            "7648833                   | 7648833                   | 0.9998\tTrue\n",
            "12q98416                  | 12q98416                  | 0.9999\tTrue\n",
            "15v72488                  | 15v72488                  | 0.9999\tTrue\n",
            "65r53444                  | 65r53444                  | 0.9996\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[7600/9000] Train loss: 0.00057, Valid loss: 0.67038, Elapsed_time: 6601.74730\n",
            "Current_accuracy : 80.415, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "38u24634                  | 38u24634                  | 0.9999\tTrue\n",
            "16v434                    | 16v44                     | 0.8692\tFalse\n",
            "63w29578                  | 63w29578                  | 0.9999\tTrue\n",
            "27v66436                  | 22v66436                  | 0.9769\tFalse\n",
            "95m59777                  | 95m59777                  | 0.9995\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[7800/9000] Train loss: 0.00058, Valid loss: 0.67850, Elapsed_time: 6775.02188\n",
            "Current_accuracy : 80.272, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "93v19888                  | 93v19888                  | 0.9999\tTrue\n",
            "75q72711                  | 75q72711                  | 0.9999\tTrue\n",
            "72u96334                  | 72u96334                  | 0.9990\tTrue\n",
            "69w25355                  | 69w25355                  | 0.9998\tTrue\n",
            "28n51133                  | 28n51133                  | 0.9998\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[8000/9000] Train loss: 0.00074, Valid loss: 0.67513, Elapsed_time: 6948.75759\n",
            "Current_accuracy : 80.201, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "57j64810                  | 57j64810                  | 0.9994\tTrue\n",
            "49n83544                  | 49n83544                  | 0.9994\tTrue\n",
            "29b61677                  | 29b61677                  | 0.9998\tTrue\n",
            "41w64918                  | 41w64918                  | 0.9995\tTrue\n",
            "85v86899                  | 85v86899                  | 0.9999\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[8200/9000] Train loss: 0.00076, Valid loss: 0.67617, Elapsed_time: 7121.96661\n",
            "Current_accuracy : 80.129, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "27v66436                  | 22v66436                  | 0.9644\tFalse\n",
            "67q15616                  | 67q15616                  | 0.9999\tTrue\n",
            "54r17299                  | 54r17299                  | 0.9996\tTrue\n",
            "65r37718                  | 65r37718                  | 0.9998\tTrue\n",
            "77m81555                  | 77m81555                  | 0.9998\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[8400/9000] Train loss: 0.00052, Valid loss: 0.69462, Elapsed_time: 7294.83163\n",
            "Current_accuracy : 80.380, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "72w74124                  | 72w74124                  | 0.9999\tTrue\n",
            "38b32720                  | 38b32720                  | 0.9996\tTrue\n",
            "41q79510                  | 41q79510                  | 0.9997\tTrue\n",
            "45i64919                  | 45i64999                  | 0.8919\tFalse\n",
            "13w12393                  | 13w12393                  | 0.9999\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[8600/9000] Train loss: 0.00049, Valid loss: 0.68978, Elapsed_time: 7467.87703\n",
            "Current_accuracy : 80.272, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.415, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "85u24266                  | 85u2466                   | 0.2867\tFalse\n",
            "28m94198                  | 28m94198                  | 0.9999\tTrue\n",
            "78l73273                  | 78l73273                  | 0.9998\tTrue\n",
            "28m69322                  | 28m69322                  | 0.9998\tTrue\n",
            "31u98557                  | 31u98557                  | 0.9937\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[8800/9000] Train loss: 0.00050, Valid loss: 0.69526, Elapsed_time: 7641.06214\n",
            "Current_accuracy : 80.559, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.559, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "17v85466                  | 17v85466                  | 0.9998\tTrue\n",
            "35c13844                  | 35c13844                  | 0.9999\tTrue\n",
            "41r81577                  | 41r81577                  | 0.9998\tTrue\n",
            "9j38314                   | 49j38314                  | 0.9952\tFalse\n",
            "7159538                   | 7159538                   | 0.9999\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "[9000/9000] Train loss: 0.00050, Valid loss: 0.69860, Elapsed_time: 7821.28088\n",
            "Current_accuracy : 80.451, Current_norm_ED  : 0.94\n",
            "Best_accuracy    : 80.559, Best_norm_ED     : 0.94\n",
            "--------------------------------------------------------------------------------\n",
            "Ground Truth              | Prediction                | Confidence Score & T/F\n",
            "--------------------------------------------------------------------------------\n",
            "4j5644                    | 4j564                     | 0.9568\tFalse\n",
            "62w87164                  | 62w87164                  | 0.9989\tTrue\n",
            "86c94810                  | 86c94810                  | 0.9841\tTrue\n",
            "89u65724                  | 89u65724                  | 0.9998\tTrue\n",
            "73w12838                  | 73w12838                  | 0.9997\tTrue\n",
            "--------------------------------------------------------------------------------\n",
            "end the training\n"
          ]
        }
      ],
      "source": [
        "!python3 /content/deep-text-recognition-benchmark/train.py \\\n",
        "--train_data /content/train_dataset --valid_data /content/validation_dataset \\\n",
        "--select_data / --batch_ratio 1 --batch_max_length 8 --valInterval 200 \\\n",
        "--num_iter 9000 --Transformation TPS --FeatureExtraction ResNet \\\n",
        "--SequenceModeling BiLSTM --Prediction Attn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X273uSVAkrU2"
      },
      "outputs": [],
      "source": [
        "!cp -r /content/saved_models /content/drive/MyDrive/datasets/Persian_License_Plate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9_7W23SH1O-"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "RdqrZl4sH1qL",
        "outputId": "1b05c060-94a8-4e66-c70d-f38538f9c857"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2000x400 with 6 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import os\n",
        "import cv2 as cv\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "test_images_path = '/content/test_images'\n",
        "images_names = os.listdir(test_images_path)\n",
        "# print(images_names)\n",
        "\n",
        "fig, axes = plt.subplots(1, len(images_names), figsize=(20, 4))\n",
        "\n",
        "for idx, ax in enumerate(axes):\n",
        "    img = cv.imread(os.path.join(test_images_path, images_names[idx]))\n",
        "    ax.imshow(cv.cvtColor(img, cv.COLOR_BGR2RGB))\n",
        "    ax.set_title(images_names[idx])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMXReWTHH3tU",
        "outputId": "a7e8862a-812e-4020-8017-f12ee4e75c28"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "model input parameters 32 100 20 1 512 256 38 25 TPS ResNet BiLSTM Attn\n",
            "loading pretrained model from /content/drive/MyDrive/datasets/Persian_License_Plate/saved_models/TPS-ResNet-BiLSTM-Attn-Seed1111/best_accuracy.pth\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n",
            "--------------------------------------------------------------------------------\n",
            "image_path               \tpredicted_labels         \tconfidence score\n",
            "--------------------------------------------------------------------------------\n",
            "/content/test_images/02.jpg\t28i21255                 \t0.9998\n",
            "/content/test_images/010.jpg\t68b31699                 \t0.9995\n",
            "/content/test_images/11.jpg\t12u32211                 \t0.5998\n",
            "/content/test_images/17.jpg\t63i56855                 \t0.9631\n",
            "/content/test_images/00074.jpg\t67922111                 \t0.3805\n"
          ]
        }
      ],
      "source": [
        "!python3 /content/deep-text-recognition-benchmark/demo.py \\\n",
        "--Transformation TPS --FeatureExtraction ResNet --SequenceModeling BiLSTM --Prediction Attn \\\n",
        "--image_folder /content/test_images \\\n",
        "--saved_model /content/drive/MyDrive/datasets/Persian_License_Plate/saved_models/TPS-ResNet-BiLSTM-Attn-Seed1111/best_accuracy.pth"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}