detector_training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "obUUZzaFDUIi"
      },
      "source": [
        "# DETECTION MODEL TRAINING\n",
        "\n",
        "- this file shows how we have trained our model\n",
        "- if you just want to test our model refer `detector_testing.ipynb`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uKDy4OhDUIk"
      },
      "source": [
        "## GETTING THE DATASET"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tW2Fm930SfYd",
        "outputId": "0375b424-dd87-4a71-a23c-33a1a8d4e876"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MUWnaMF-DUIm"
      },
      "source": [
        "`x_ultimate.zip` is the COCO 2017 test dataset images cropped to 256x256 and pixelated"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "60Q5_wXxSfYg"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/ULTIMATE_DATASET/x_ultimate.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SZUdy8S8SfYh",
        "outputId": "3dbf0a62-8930-4744-c39a-dd031e9917a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "80820\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "print(len(os.listdir('./x_ultimate/')))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xgsEwEqFDUIn"
      },
      "source": [
        "upgrading these libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0fUhRm8USfYh"
      },
      "outputs": [],
      "source": [
        "%pip install --upgrade pip\n",
        "%pip install --upgrade tensorflow\n",
        "%pip install --upgrade scikit-learn\n",
        "%pip install --upgrade pydot\n",
        "%pip install --upgrade matplotlib\n",
        "\n",
        "## IN CASE YOU ARE FACING ISSUES WHILE LOADING MODELS OR IMPORTING LIBRARIES YOU CAN\n",
        "## USE THE VERSIONS OF THESE LIBRARIES ON WHICH OUR MODEL WAS CREATED\n",
        "\n",
        "# %pip install --upgrade pip==24.1.1\n",
        "# %pip install --upgrade ipykernel==5.5.6\n",
        "# %pip install --upgrade numpy==1.25.2\n",
        "# %pip install --upgrade pandas==2.0.3\n",
        "# %pip install --upgrade tensorflow==2.16.2\n",
        "# %pip install --upgrade keras==3.4.1\n",
        "# %pip install --upgrade scikit-learn==1.5.0\n",
        "# %pip install --upgrade matplotlib==3.9.0\n",
        "# %pip install --upgrade pydot==2.0.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "jBkLpQEgSfYi"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as tfk\n",
        "import tensorflow.keras.layers as tfl\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa-HJTDSDUIo"
      },
      "source": [
        "getting the `image_paths` and `image_labels` as lists"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjxEaUtvSfYi",
        "outputId": "c1a1a4a6-aef6-4fb9-8d0c-af41a7ba874b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "80820\n",
            "80820\n",
            "['./x_ultimate/300021196t_d3.png', './x_ultimate/300021197o_real.png', './x_ultimate/300021198t_d8.png', './x_ultimate/300021199o_real.png', './x_ultimate/300021200t_d4.png']\n",
            "[1, 0, 1, 0, 1]\n"
          ]
        }
      ],
      "source": [
        "NON_PIXELATED = 0\n",
        "PIXELATED = 1\n",
        "class_names = ['Non-Pixelated', 'Pixelated']\n",
        "\n",
        "image_dir = './x_ultimate/'\n",
        "image_basenames = sorted(os.listdir(image_dir))\n",
        "\n",
        "image_paths = [os.path.join(image_dir, bname) for bname in image_basenames]\n",
        "image_labels = [NON_PIXELATED if bname.split('_')[1][0] == 'r' else PIXELATED for bname in image_basenames]\n",
        "\n",
        "total_size = len(image_paths)\n",
        "\n",
        "print(len(image_paths))\n",
        "print(len(image_labels))\n",
        "\n",
        "print(image_paths[-5:])\n",
        "print(image_labels[-5:])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ydYyslyJDUIo"
      },
      "source": [
        "function to load the image and preprocess it to hqave values in [0,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "oNKRuWsLSfYj"
      },
      "outputs": [],
      "source": [
        "@tf.function\n",
        "def load_image(image_path):\n",
        "    image = tf.io.read_file(image_path)\n",
        "    image = tf.image.decode_image(image, channels=3, dtype=tf.float32, expand_animations=False)\n",
        "    return image\n",
        "\n",
        "@tf.function\n",
        "def load_data(image_path, image_label):\n",
        "    return load_image(image_path), image_label"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k_tOmPKaDUIo"
      },
      "source": [
        "creating the train, test, validation datasets and batching it"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tw7PM6mFSfYj",
        "outputId": "4d29b8bf-fab9-40b6-a1b9-66523a493467"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "884 190 190\n"
          ]
        }
      ],
      "source": [
        "dataset = tf.data.Dataset.from_tensor_slices((image_paths, image_labels))\n",
        "\n",
        "train_val_size = int(0.85 * total_size)\n",
        "train_size     = int(0.70 * total_size)\n",
        "\n",
        "train_val_dataset = dataset.take(train_val_size)\n",
        "test_dataset      = dataset.skip(train_val_size)\n",
        "\n",
        "train_dataset = train_val_dataset.take(train_size)\n",
        "val_dataset   = train_val_dataset.skip(train_size)\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "train_dataset = train_dataset.shuffle(buffer_size=dataset.cardinality(), seed=61, reshuffle_each_iteration=True)\\\n",
        "                             .map(load_data, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                             .batch(batch_size, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                             .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "val_dataset   =   val_dataset.shuffle(buffer_size=dataset.cardinality(), seed=62, reshuffle_each_iteration=True)\\\n",
        "                             .map(load_data, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                             .batch(batch_size, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                             .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "test_dataset  =  test_dataset.shuffle(buffer_size=dataset.cardinality(), seed=63, reshuffle_each_iteration=True)\\\n",
        "                             .map(load_data, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                             .batch(batch_size, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                             .prefetch(buffer_size=tf.data.AUTOTUNE)\n",
        "\n",
        "print(train_dataset.cardinality().numpy(), val_dataset.cardinality().numpy(), test_dataset.cardinality().numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fd_WzQXBDUIp"
      },
      "source": [
        "this is the full model architecture, refer to the `detector_005_1.png` for a visual description"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZIehu3OKSfYk",
        "outputId": "0a35a41c-a2b7-403f-aa98-c1b01a88bae2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"detector\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"detector\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)       │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_1              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)       │              \u001b[38;5;34m59\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)       │              \u001b[38;5;34m32\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m)       │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_2              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)      │             \u001b[38;5;34m216\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)      │              \u001b[38;5;34m64\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)      │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_3              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)      │             \u001b[38;5;34m688\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_3                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)      │             \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)      │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_4              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)      │           \u001b[38;5;34m2,400\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_4                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)      │             \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)      │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_5              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │           \u001b[38;5;34m8,896\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_5                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │             \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)     │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_6              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)     │          \u001b[38;5;34m34,176\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_6                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)     │           \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)     │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_7              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)     │         \u001b[38;5;34m133,888\u001b[0m │\n",
              "│ (\u001b[38;5;33mSeparableConv2D\u001b[0m)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_7                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)     │           \u001b[38;5;34m2,048\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m)            │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)     │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_max_pooling_cl                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "│ (\u001b[38;5;33mGlobalMaxPooling2D\u001b[0m)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1_cl (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │          \u001b[38;5;34m32,832\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2_cl (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m65\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_1              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">59</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_1                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)       │              <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)       │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_2              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">216</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_2                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)      │              <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_3              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">688</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_3                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_4              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,400</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_4                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)      │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_5              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">8,896</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_5                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)     │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_6              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)     │          <span style=\"color: #00af00; text-decoration-color: #00af00\">34,176</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_6                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)     │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ separable_convolution_7              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">133,888</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SeparableConv2D</span>)                    │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ batch_normalization_7                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,048</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)            │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)     │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ global_max_pooling_cl                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalMaxPooling2D</span>)                 │                             │                 │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_1_cl (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">32,832</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_2_cl (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">65</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m217,284\u001b[0m (848.77 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">217,284</span> (848.77 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m215,252\u001b[0m (840.83 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">215,252</span> (840.83 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m2,032\u001b[0m (7.94 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,032</span> (7.94 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def translate_encoder(block_id, x, filters, kernel_size=3, activation='relu'):\n",
        "  x = tfl.SeparableConv2D(filters=filters, kernel_size=kernel_size, strides=(1, 1), padding='same', activation=None, use_bias=True, name=f'separable_convolution_{block_id}')(x)\n",
        "  x = tfl.BatchNormalization(epsilon=1e-3, momentum = 0.999, name=f'batch_normalization_{block_id}')(x)\n",
        "  return tfl.Activation(activation=activation, name=f'activation_{block_id}')(x)\n",
        "\n",
        "def encoder_block(block_id, x, filters, kernel_size=3, strides=2, padding='same', activation='relu'):\n",
        "  x = tfl.SeparableConv2D(filters=filters, kernel_size=kernel_size, strides=strides, padding=padding, activation=None, use_bias=True, name=f'separable_convolution_{block_id}')(x)\n",
        "  x = tfl.BatchNormalization(epsilon=1e-3, momentum = 0.999, name=f'batch_normalization_{block_id}')(x)\n",
        "  return tfl.Activation(activation=activation, name=f'activation_{block_id}')(x)\n",
        "\n",
        "def classifier_block(block_id, x, units=64, activation='relu'):\n",
        "  x = tfl.GlobalMaxPooling2D(name=f'global_max_pooling_{block_id}')(x)\n",
        "  x = tfl.Dense(units=units, activation=activation, name=f'dense_1_{block_id}')(x)\n",
        "  return tfl.Dense(units=1, name=f'dense_2_{block_id}')(x)\n",
        "\n",
        "input_shape = (None, None, 3)\n",
        "inputs = tfk.Input(input_shape, name='input_layer')\n",
        "\n",
        "# encoder\n",
        "t0 = translate_encoder(1, inputs, 8)\n",
        "t1 = translate_encoder(2, t0, 16)\n",
        "e0 = encoder_block(3, t1, 32)\n",
        "e1 = encoder_block(4, e0, 64)\n",
        "e2 = encoder_block(5, e1, 128)\n",
        "e3 = encoder_block(6, e2, 256)\n",
        "encoder_outputs = encoder_block(7, e3, 512)\n",
        "\n",
        "# classifier\n",
        "classifier_output = classifier_block('cl', encoder_outputs)\n",
        "\n",
        "detector = tfk.Model(inputs, classifier_output, name='detector')\n",
        "\n",
        "detector.summary()\n",
        "# tfk.utils.plot_model(detector, to_file='/content/drive/MyDrive/DETECTOR_005/detector_005_1.png', show_layer_activations=True, show_layer_names=True, show_shapes=True, show_trainable=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "RpR8HdyMhukJ"
      },
      "outputs": [],
      "source": [
        "detector.compile(\n",
        "    optimizer=tfk.optimizers.Adam(learning_rate=1e-5),\n",
        "    loss=tfk.losses.BinaryCrossentropy(from_logits=True),\n",
        "    metrics=[\n",
        "        tfk.metrics.BinaryAccuracy(threshold=0.5, name='accuracy'),\n",
        "        tfk.metrics.Precision(thresholds=0.0, name='precision'),\n",
        "        tfk.metrics.Recall(thresholds=0.0, name='recall')\n",
        "        ]\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bS79GuuDDUIp"
      },
      "source": [
        "testing the model before training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "outputId": "640b717a-602b-4704-adf2-c6f5e7b1c8e9",
        "id": "Mju1lBL4Xs8G"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m190/190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 263ms/step - accuracy: 0.4084 - loss: 0.6931 - precision: 0.5916 - recall: 1.0000\n"
          ]
        }
      ],
      "source": [
        "# before training\n",
        "loss0, accuracy0, precision0, recall0 = detector.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ukWt43z6SfYk"
      },
      "outputs": [],
      "source": [
        "early_stopping = tfk.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=1e-4,\n",
        "    patience=10,\n",
        "    verbose=1,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=False,\n",
        "    start_from_epoch=5,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gR4pcUKJDUIp"
      },
      "source": [
        "## training for 25 epochs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5zEMBbwbCjkO",
        "outputId": "78924fbe-a6f1-4df7-ec0a-c15cf0260363"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m364s\u001b[0m 383ms/step - accuracy: 0.6369 - loss: 0.6865 - precision: 0.6929 - recall: 0.8218 - val_accuracy: 0.4014 - val_loss: 0.6897 - val_precision: 0.5986 - val_recall: 1.0000\n",
            "Epoch 2/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 296ms/step - accuracy: 0.7513 - loss: 0.5176 - precision: 0.7669 - recall: 0.8355 - val_accuracy: 0.4014 - val_loss: 0.6852 - val_precision: 0.5986 - val_recall: 1.0000\n",
            "Epoch 3/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m334s\u001b[0m 310ms/step - accuracy: 0.7615 - loss: 0.4898 - precision: 0.7876 - recall: 0.8301 - val_accuracy: 0.4014 - val_loss: 0.6784 - val_precision: 0.5986 - val_recall: 1.0000\n",
            "Epoch 4/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 296ms/step - accuracy: 0.7784 - loss: 0.4600 - precision: 0.8079 - recall: 0.8345 - val_accuracy: 0.4014 - val_loss: 0.6749 - val_precision: 0.5986 - val_recall: 1.0000\n",
            "Epoch 5/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m304s\u001b[0m 343ms/step - accuracy: 0.7916 - loss: 0.4320 - precision: 0.8233 - recall: 0.8411 - val_accuracy: 0.5986 - val_loss: 0.7169 - val_precision: 0.5986 - val_recall: 1.0000\n",
            "Epoch 6/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 296ms/step - accuracy: 0.8094 - loss: 0.3992 - precision: 0.8417 - recall: 0.8554 - val_accuracy: 0.6020 - val_loss: 0.7679 - val_precision: 0.5989 - val_recall: 1.0000\n",
            "Epoch 7/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 295ms/step - accuracy: 0.8324 - loss: 0.3618 - precision: 0.8603 - recall: 0.8748 - val_accuracy: 0.7863 - val_loss: 0.4965 - val_precision: 0.6958 - val_recall: 0.9771\n",
            "Epoch 8/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 294ms/step - accuracy: 0.8586 - loss: 0.3187 - precision: 0.8785 - recall: 0.8985 - val_accuracy: 0.8386 - val_loss: 0.3491 - val_precision: 0.9018 - val_recall: 0.8639\n",
            "Epoch 9/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m261s\u001b[0m 295ms/step - accuracy: 0.8770 - loss: 0.2823 - precision: 0.8933 - recall: 0.9128 - val_accuracy: 0.8005 - val_loss: 0.3667 - val_precision: 0.9610 - val_recall: 0.7775\n",
            "Epoch 10/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 295ms/step - accuracy: 0.8999 - loss: 0.2404 - precision: 0.9110 - recall: 0.9281 - val_accuracy: 0.8154 - val_loss: 0.3446 - val_precision: 0.9709 - val_recall: 0.7871\n",
            "Epoch 11/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m302s\u001b[0m 341ms/step - accuracy: 0.9115 - loss: 0.2114 - precision: 0.9242 - recall: 0.9374 - val_accuracy: 0.8404 - val_loss: 0.3138 - val_precision: 0.9744 - val_recall: 0.8075\n",
            "Epoch 12/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m281s\u001b[0m 295ms/step - accuracy: 0.9230 - loss: 0.1900 - precision: 0.9337 - recall: 0.9442 - val_accuracy: 0.8875 - val_loss: 0.2343 - val_precision: 0.9674 - val_recall: 0.8782\n",
            "Epoch 13/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 294ms/step - accuracy: 0.9303 - loss: 0.1718 - precision: 0.9386 - recall: 0.9505 - val_accuracy: 0.9098 - val_loss: 0.2017 - val_precision: 0.9674 - val_recall: 0.9026\n",
            "Epoch 14/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m263s\u001b[0m 295ms/step - accuracy: 0.9371 - loss: 0.1592 - precision: 0.9448 - recall: 0.9553 - val_accuracy: 0.8984 - val_loss: 0.2185 - val_precision: 0.9769 - val_recall: 0.8852\n",
            "Epoch 15/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m264s\u001b[0m 298ms/step - accuracy: 0.9417 - loss: 0.1487 - precision: 0.9473 - recall: 0.9585 - val_accuracy: 0.9330 - val_loss: 0.1610 - val_precision: 0.9647 - val_recall: 0.9366\n",
            "Epoch 16/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 293ms/step - accuracy: 0.9487 - loss: 0.1315 - precision: 0.9539 - recall: 0.9635 - val_accuracy: 0.9437 - val_loss: 0.1456 - val_precision: 0.9613 - val_recall: 0.9490\n",
            "Epoch 17/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m296s\u001b[0m 334ms/step - accuracy: 0.9513 - loss: 0.1257 - precision: 0.9564 - recall: 0.9659 - val_accuracy: 0.9503 - val_loss: 0.1380 - val_precision: 0.9513 - val_recall: 0.9640\n",
            "Epoch 18/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m296s\u001b[0m 335ms/step - accuracy: 0.9567 - loss: 0.1171 - precision: 0.9598 - recall: 0.9692 - val_accuracy: 0.9500 - val_loss: 0.1304 - val_precision: 0.9657 - val_recall: 0.9554\n",
            "Epoch 19/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m287s\u001b[0m 296ms/step - accuracy: 0.9592 - loss: 0.1094 - precision: 0.9641 - recall: 0.9702 - val_accuracy: 0.9521 - val_loss: 0.1256 - val_precision: 0.9702 - val_recall: 0.9567\n",
            "Epoch 20/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m260s\u001b[0m 294ms/step - accuracy: 0.9605 - loss: 0.1024 - precision: 0.9640 - recall: 0.9724 - val_accuracy: 0.9568 - val_loss: 0.1172 - val_precision: 0.9672 - val_recall: 0.9631\n",
            "Epoch 21/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m259s\u001b[0m 292ms/step - accuracy: 0.9630 - loss: 0.0965 - precision: 0.9679 - recall: 0.9732 - val_accuracy: 0.9593 - val_loss: 0.1116 - val_precision: 0.9663 - val_recall: 0.9675\n",
            "Epoch 22/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m262s\u001b[0m 292ms/step - accuracy: 0.9670 - loss: 0.0901 - precision: 0.9701 - recall: 0.9773 - val_accuracy: 0.9593 - val_loss: 0.1112 - val_precision: 0.9726 - val_recall: 0.9631\n",
            "Epoch 23/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m303s\u001b[0m 339ms/step - accuracy: 0.9696 - loss: 0.0835 - precision: 0.9730 - recall: 0.9785 - val_accuracy: 0.9639 - val_loss: 0.1050 - val_precision: 0.9625 - val_recall: 0.9755\n",
            "Epoch 24/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m256s\u001b[0m 290ms/step - accuracy: 0.9707 - loss: 0.0786 - precision: 0.9734 - recall: 0.9804 - val_accuracy: 0.9653 - val_loss: 0.0994 - val_precision: 0.9703 - val_recall: 0.9712\n",
            "Epoch 25/25\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m257s\u001b[0m 290ms/step - accuracy: 0.9703 - loss: 0.0780 - precision: 0.9721 - recall: 0.9793 - val_accuracy: 0.9666 - val_loss: 0.1001 - val_precision: 0.9608 - val_recall: 0.9802\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ],
      "source": [
        "history = detector.fit(train_dataset,\n",
        "                    epochs=25,\n",
        "                    validation_data=val_dataset,\n",
        "                    initial_epoch=0,\n",
        "                    callbacks=[early_stopping])\n",
        "\n",
        "detector.save('/content/drive/MyDrive/DETECTOR_005/detector_005_1_25.keras')\n",
        "detector.save('/content/drive/MyDrive/DETECTOR_005/detector_005_1_25.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olb3XfNRDUIq"
      },
      "source": [
        "## RESULTS AFTER 25 EPOCHS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hQglo04sSfYl"
      },
      "outputs": [],
      "source": [
        "def history_results(history, par1='loss', par2='val_loss', title='Loss'):\n",
        "    plt.plot(history.history[par1])\n",
        "    plt.plot(history.history[par2])\n",
        "    plt.title(title)\n",
        "    plt.ylabel(par1)\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'val'], loc='upper left')\n",
        "    plt.grid()\n",
        "    plt.show()\n",
        "    return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "0Su1nzvJSfYl",
        "outputId": "dc379b94-d0ae-423b-b02c-e6b487632cc1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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xWZ1qBYKq8r2fOgiefmc8LTg4mMsuu4zZs2czePBgAL788ktCQ0O5+OKLsVqtdOvWrfT8559/nm+++Ybvv/+eCRMmVGXFZ8/lMnp20rdBQKQxBd1qq9wlS25pZe4Hx1Gwe1dBoSIi4g7Uw+Mmbr31Vr766isKCgoA+OSTT7jpppuwWq3k5OTw2GOP0aFDB4KCgvD39yc+Pt7cHp41H8KGOWCxwfUzwb9R5a/p2xC8AgGXpqaLiEgZ6uE5E7uv0dNyFpxOJ1nZ2QQGBGC1VkGWtPue9akjRozA5XIxd+5cevfuzR9//MFrr70GwGOPPUZsbCyvvPIKrVu3xsfHh+uvv57CwsLK11gRe/6Anx439gc/A837V811LRbjtlbSemPgclj7qrmuiIjUeQo8Z2KxnNVtJQCcTrAXG+dXReA5B97e3lx77bV88skn7Ny5k3bt2nHeeecBsGTJEu644w6uueYaAHJycti7d2+N1lfq4Dr49GYoLoAOI6D/g1V7/ZBWxwKPBi6LiMhxCjxu5NZbb+XKK69k8+bN3HbbbaXH27Rpw9dff82IESOwWCw8/fTTJ83oqhGHdsHH10FhNkQPhGvfq/pgqJlaIiJSDo3hcSOXXHIJISEhbNu2jVtuuaX0+NSpUwkODqZ///6MGDGC4cOHl/b+1JisJPhoJOSlQ0RXuGl29Qwq1tOWRUSkHOrhcSNWq5WDB08ebxQdHc2vv/5a5tj48ePL/F6tt7jyj8DH10JGghFIbvsKvAOr570atjJ+amq6iIicoFb08EyfPp3o6Gi8vb3p27cvK1asOOW5gwYNwmKxnLRdccUVNVixnLXCfJh9E6RuAf8IuP1b8A+rvvcr6eHJTISigup7HxERqVNMDzxz5swhJiaGSZMmsWbNGrp168bw4cNJTU0t9/yvv/6apKSk0m3Tpk3YbDZuuOGGGq5czsjlgl+ehMTl4N0Abv8GgptX73v6NQLPADQ1XURETmR64Jk6dSrjxo1j7NixdOzYkRkzZuDr68vMmTPLPT8kJISIiIjSLTY2Fl9fXwWe2sblgvzDsG8JePjALV9AeMfqf9+SqemggcsiIlLK1DE8hYWFrF69mokTJ5Yes1qtDBkyhGXLlp3VNd5//31uuukm/PzKnzpeUFBQ+jA+gKysLAAcDgcOh6PMuQ6HA5fLhdPprNAsJtexpSBKrlGfuXJSoDAXFzaKrpuJK/I8+Et7V5WSf44lP23BLbAmb6A4bTvOlkOq5T3l5HaXmqF2N4fa3Rwntntl297UwJOenk5xcTHh4eFljoeHh7N169Yzvn7FihVs2rSJ999//5TnTJkyhcmTJ590fP78+fj6ln2wn4eHBxEREWRnZ1fqoXzZ2dkVfq078HJkQN5hALZEXsf+7Q7Y/lO1v29sbCwAHQ47aQskrFvIhkPR1f6+9V1Ju0vNUrubQ+1ujtjYWPLy8ip1jTo9S+v999+nS5cu9OnT55TnTJw4kZiYmNLfs7KyiIqKYtiwYQQGlp0pVFxczO7du7FarSf97Wy4XC6ys7MJCAjAYrGc8+vdgSX/EJa8IxxygMsrkE7XPUFXW+XWyDoTh8NBbGwsQ4cOxW63Y1mfAT/+QPMAJ00vv7xa37s++2u7S81Qu5tD7W6OE9s9Pz+/UtcyNfCEhoZis9lISUkpczwlJYWIiIjTvjY3N5fPPvuM55577rTneXl54eXlddJxu91+0pfWbrcTHBxMeno6VqsVX1/fcwouTqeTwsJCCgoKqmZpibrmaBauzP3kOSA1p4jgSGPmXU0p/WfaqC0A1iO7sepfTNWuvP8tSfVTu5tD7W4Ou91OUVFRpa5hauDx9PSkZ8+exMXFMXLkSMAIDXFxcWdcxfuLL76goKCgzBOFq0JJ0DrVLLFTcrlwuZzkHy3Ax8en/vXwFB2FnDTABR7eBDVpc8bQWm1CTlg1vagAPE4OvCIiUr+YfksrJiaGMWPG0KtXL/r06cO0adPIzc1l7NixAIwePZomTZowZcqUMq97//33GTlyJA0bNqzSeiwWC5GRkYSFhZ3bAKlDu+HTm3BYvPAIaoIlIAICIoxnzwSEH9/3awQenlVas+lSt8A394IjD1pejH3Eq9jsJn5G/zDw9IfCHDiyr7THR0RE6i/TA8+oUaNIS0vjmWeeITk5me7duzNv3rzSgcwJCQkn3R7atm0bixcvZv78+dVWl81mw3YuY0+OJkNOIt4A2TtPc6LFCD8NmhpbYBNoEHX89wZR4BtiTK+uC9J3wmc3GEtGtLgQrnrF/EBXMjU9eaMxNV2BR0Sk3jM98ABMmDDhlLewFi5ceNKxdu3alU4BrzVaXYLjiQR+/+FTLureCo/cJOOWSuZ+46m/mQeM/eICyE4ytv0ry7+Wh48RfkLbGs+uCesAYZ2MZRNstejecdZB+OgaI+xEdoNRn9Se20chLY8HHhERqfdqReBxG3ZfcrwjcbUcBOUNanO5IDf9WADaf3zLOmE/JwWK8uHQDmPbNvf4622eRggK6wBhHY9tHSCoWc33COUdho+uhcwEaNgabq3G9bEqIqRkTS0tIioiIgo8NctiAf9GxtbkFKuVFxVA1gFj7EnaNkjdDKnxxlaYAymbjO1EngEQ1v54CAo/9tMvtHo+R2EuzB4FafEQEGksGeHfqHreq6JKBi6rh0dERFDgqX08vIz/WIe0hFYXHz/udBo9Q6lbjC1lixGC0rdDYbZxe+yvt8j8wo71BnU4Nmg63BjQ6x9u/M0vFKzn+IycYgd8Pgb2rwDvICPsBDWr9MeuciWB55B6eERERIGn7rBajYU3g5tDu8uOHy8qNG7bpJT0BB0LREf2Qm4q7EmFPYvKv6bFCr6hx4JQo5MDUcm+fxj4BBu35L69H3bGGuOMbv3CCFO1UcNjt7QyE402MnsgtYiImEqBp67z8Dzei3Oigpzjt8TSd0BOqjE+KDft2M90cDmNUJSbCinlX76U1W6seJ6XDlYPGPURRJ36Cdem8w8Hux84ciEjAUJbm12RiIiYSIHHXXn5Q9Oexlae4iLIO2SEn9IwlHp8P+eE/aMZ4HQYYQcLjHwL2gytyU9z7iwW47ZWykajB0yBR0SkXlPgqa9sHsceiBh+5nOLCo73DPmHG1Pm64KQFscCjwYui4jUdwo8cmYeXscfjFiXaOCyiIgcUw9XuJR6o2Tgsnp4RETqPQUecV96Fo+IiByjwCPuq+RpyxkJxvODRESk3lLgEfcVEGE8L8hVbIQeERGptxR4xH2VTE0HDVwWEannFHjEvTXUOB4REVHgEXengcsiIoICj7i7koHLh3VLS0SkPlPgEfemHh4REUGBR9xdSeA5sk9T00VE6jEFHnFvAZGami4iIgo84uasVmMRUYDDe8ytRURETKPAI+6vdByPBi6LiNRXCjzi/jRwWUSk3lPgEfenpy2LiNR7Cjzi/hqWPItHPTwiIvWVAo+4v5Ienox9UFxkbi0iImIKBR5xfwGNwcMbnEWQqanpIiL1kQKPuD+rFYJLpqbrtpaISH2kwCP1Q+nAZQUeEZH6SIFH6oeGmpouIlKfKfBI/aBn8YiI1GsKPFI/6GnLIiL1mgKP1A8hx57Fc0RT00VE6iMFHqkfApuAzQucDshMNLsaERGpYQo8Uj+UWTVd43hEROobBR6pPzRwWUSk3lLgkfpDgUdEpN5S4JH6Q4FHRKTeMj3wTJ8+nejoaLy9venbty8rVqw47fkZGRmMHz+eyMhIvLy8aNu2LT/99FMNVSt1WunTljU1XUSkvvEw883nzJlDTEwMM2bMoG/fvkybNo3hw4ezbds2wsLCTjq/sLCQoUOHEhYWxpdffkmTJk3Yt28fQUFBNV+81D0NS6am7wVnMVhtppYjIiI1x9TAM3XqVMaNG8fYsWMBmDFjBnPnzmXmzJk8+eSTJ50/c+ZMDh8+zNKlS7Hb7QBER0fXZMlSlwU2AZsnFBdC5n4Ibm52RSIiUkNMu6VVWFjI6tWrGTJkyPFirFaGDBnCsmXLyn3N999/T79+/Rg/fjzh4eF07tyZF154geLi4poqW+oyqw2Co419PXFZRKReMa2HJz09neLiYsLDw8scDw8PZ+vWreW+Zvfu3fz666/ceuut/PTTT+zcuZP7778fh8PBpEmTyn1NQUEBBQUFpb9nZWUB4HA4cDgcVfRpKL3miT+lZpxLu9uCW2BN305x2g6czQZWd2luTd93c6jdzaF2N8eJ7V7Ztjf1lta5cjqdhIWF8c4772Cz2ejZsycHDhzg5ZdfPmXgmTJlCpMnTz7p+Pz58/H19a2WOmNjY6vlunJ6Z9PunTKgNbBn9a9sTomo9prqA33fzaF2N4fa3RyxsbHk5eVV6hqmBZ7Q0FBsNhspKSlljqekpBARUf5/iCIjI7Hb7dhsxwebdujQgeTkZAoLC/H09DzpNRMnTiQmJqb096ysLKKiohg2bBiBgYFV9GkMDoeD2NhYhg4dWjrGSKrfubS7dXUyzPuFlkEuml9+eQ1V6J70fTeH2t0candznNju+fn5lbqWaYHH09OTnj17EhcXx8iRIwGjBycuLo4JEyaU+5oLLriA2bNn43Q6sVqN4Ufbt28nMjKy3LAD4OXlhZeX10nH7XZ7tX1pq/Pacmpn1e6N2gBgPbIXq/4ZVQl9382hdjeH2t0cdrudoqLKLfxs6nN4YmJiePfdd/nggw+Ij4/nvvvuIzc3t3TW1ujRo5k4cWLp+ffddx+HDx/moYceYvv27cydO5cXXniB8ePHm/URShUUFfPR8gSWp1rMLkVOp+RZPEf2GFPTRUSkXjB1DM+oUaNIS0vjmWeeITk5me7duzNv3rzSgcwJCQmlPTkAUVFR/PLLLzzyyCN07dqVJk2a8NBDD/H3v//drI9Q6of1STw3dyv+ditPFhbRQP8PoHZqEAVWuzE1PesABDUzuyIREakBpg9anjBhwilvYS1cuPCkY/369WP58uXVXNW5u7p7Y/6zYDuJR/L5+M9Exl/S1uySpDwlU9MP7TCWmFDgERGpF0xfWsJd2G1WJlxs3C55b/Fecgoqd69RqlHJE5e1xISISL2hwFOFruoaSZi3iyN5Dv63eI/Z5cipaBFREZF6R4GnCnnYrFwa5QTg3T92k5mvB1TVSgo8IiL1jgJPFevR0EWbMD+yjhbx/h/6D2qtpMAjIlLvKPBUMasFHrykNQAzl+zlSG6hyRXJSUoDzx5wOs2tRUREaoQCTzUY1iGMjpGB5BQU8fbv6kWodUqnphcYU9NFRMTtKfBUA6vVwiNDjWnpHyzdS1p2wRleITXK5gHBzY193dYSEakXFHiqyZAOYXRr2oB8RzFvL9L051qn9LaW/tmIiNQHCjzVxGI53svz0fJ9pGQdNbkiKSPk2LN41MMjIlIvKPBUo4vaNqJn82AKipy8+dtOs8uRE504cFlERNyeAk81slgsPHqsl+fTFYkcyKjc0vZShRoeCzx62rKISL2gwFPN+rcO5fyWIRQWO3njV/Xy1BplVk3X1HQREXenwFMDHh3WDoAvViWScCjP5GoEgAbNwOoBRUch+6DZ1YiISDVT4KkBvaNDGNgmlCKni9d/3WF2OQLG1PQgTU0XEakvFHhqSEkvz9dr9rM7LcfkagTQEhMiIvWIAk8N6R4VxOD2YThd8J849fLUCg2PTU3XwGUREbenwFODSp7L8/36g2xPyTa5GlEPj4hI/aHAU4M6N2nApZ0icLlg2oLtZpcjCjwiIvWGAk8Ne2RoWywW+GljMlsOZpldTv2mVdNFROoNBZ4a1i4igCu6RALwmnp5zBXUDCw2KMqHnGSzqxERkWqkwGOCh4e0xWqB2C0pbNifYXY59ZfNboQe0MBlERE3p8BjgtZh/ozs3gSAqbHq5TFVQy0iKiJSHyjwmOTBwW2wWS0s3JbG6n1HzC6n/iodx6MeHhERd6bAY5LoUD+uP68pAFNjt5lcTT0Woh4eEZH6QIHHRA8Mbo3dZmHJzkMs333I7HLqpxNnaomIiNtS4DFR02BfRvWOAmDq/O24XC6TK6qHTnwWj9pfRMRtKfCYbMLFbfD0sLJi72EW70w3u5z6p2RquiMPsjU1XUTEXSnwmCyigTe39jWmRr+qXp6a5+EJQUYvmwYui4i4LwWeWuC+Qa3wtltZl5jBb9tSzS6n/tHAZRERt6fAUwuEBXgzpl80YDyXR708NUxraomIuD0Fnlribxe1ws/TxqYDWczfkmJ2OfVLSeDR05ZFRNyWAk8tEeLnyR0XRAPwWux2nE718tSY0qcta2q6iIi7UuCpRcYNbEmAlwdbk7P5aVOS2eXUH5qaLiLi9hR4apEgX0/uGtgCgGkLdlCsXp6aEdQcLFZw5EKObieKiLgjBZ5a5s4BLWjgY2dnag6frUwwu5z6wcMTGpRMTdfAZRERd6TAU8sEetu59yJjTMk/vtnEf+N2aNZWTdDAZRERt6bAUwuNG9iCMf2aA/Bq7Hbu/2QNuQVFJlfl5hrqWTwiIu5MgacW8rBZmXx1Z168tgt2m4WfNyVz3VtLSTycZ3Zp7qt04LJ6eERE3FGtCDzTp08nOjoab29v+vbty4oVK0557qxZs7BYLGU2b2/vGqy25tzUpxmf3XM+of5ebE3O5qo3FrNU621VDz1tWUTErZkeeObMmUNMTAyTJk1izZo1dOvWjeHDh5OaeuolFgIDA0lKSird9u3bV4MV16yezUP44YEL6Nq0AUfyHNw+cwWzluzRuJ6qVtrDs0dT00VE3JDpgWfq1KmMGzeOsWPH0rFjR2bMmIGvry8zZ8485WssFgsRERGlW3h4eA1WXPMiG/jw+d/6cU2PJhQ7XTz7wxb+/tUGCoqKzS7NfQQfm5pemAM5Ws9MRMTdeJj55oWFhaxevZqJEyeWHrNarQwZMoRly5ad8nU5OTk0b94cp9PJeeedxwsvvECnTp3KPbegoICCgoLS37OysgBwOBw4HI4q+iSUXvPEn1XJBvz7mo60D/fj379s5/NV+9mRks0bN3cnLMCryt+vLqmadrfiEdgUS2YCRWnbcXmHVE1xbqw6v+9yamp3c6jdzXFiu1e27S0uE++NHDx4kCZNmrB06VL69etXevyJJ55g0aJF/Pnnnye9ZtmyZezYsYOuXbuSmZnJK6+8wu+//87mzZtp2rTpSec/++yzTJ48+aTjs2fPxtfXt2o/UA3ZmmFh1nYr+cUWGthd3NWumOYBZldV9/Xb+W/CsjezttndJDS80OxyRETkBHl5edxyyy1kZmYSGBh4zq+vc4HnrxwOBx06dODmm2/m+eefP+nv5fXwREVFkZ6eXqEGO1MtsbGxDB06FLvdXqXX/qt9h/L42ydr2ZWWi6eHlX9d1ZFrejSu1vesraqq3a0/P45tzf8o7v8wzov/WYUVuqea/L7LcWp3c6jdzXFiu+fn5xMaGlrhwGPqLa3Q0FBsNhspKWUf55+SkkJERMRZXcNut9OjRw927txZ7t+9vLzw8jr5lo/dbq+2L211XrtE64gGfDv+Ah6Zs54F8Sk88fUmtqfm8uRl7fGwmT40yxSVbvcI47aoLWkNNv0L7azVxPddTqZ2N4fa3Rx2u52ioso9j87U/zJ6enrSs2dP4uLiSo85nU7i4uLK9PicTnFxMRs3biQyMrK6yqy1ArztvHN7Tx68pDUA7y3ew9hZK8nIKzS5sjqqxbHbWAl/giPf3FpERKRKmd4VEBMTw7vvvssHH3xAfHw89913H7m5uYwdOxaA0aNHlxnU/NxzzzF//nx2797NmjVruO2229i3bx933323WR/BVFarhZhh7Xjz1vPwsdv4Y0c6V09fwvaUbLNLq3tC24J/BBQXQOKZb6eKiEjdYeotLYBRo0aRlpbGM888Q3JyMt27d2fevHmlU80TEhKwWo/nsiNHjjBu3DiSk5MJDg6mZ8+eLF26lI4dO5r1EWqFy7tEEt3Qj3s+WsW+Q3lcM30JU0d1Z3ins7s1KIDFAi0vgg1zYPciaDnI7IpERKSKmB54ACZMmMCECRPK/dvChQvL/P7aa6/x2muv1UBVdU/HxoF8P2EA4z9Zw7Ldh/jbR6t5ZEhbHrikNVarxezy6oYWxwLPnkVmVyIiIlXI9FtaUrVC/Dz58K4+3NE/GoDXFhiLj2Yf1bMjzkrLi4yfB9dCfoappYiISNVR4HFDdpuVZ6/qxL+vMxYfnbc5mcv+8wcr9hw2u7Tar0FTY10tlxP2LTG7GhERqSIKPG5sVO9mfHZPP5oG+7D/SD6j3lnGiz9v1ZIUZ1LSy7Nbt7VERNyFAo+b69k8mJ8fGsgNPZvicsGMRbsYOX0p25I1i+uUWhwLPBrHIyLiNhR46oEAbzsv39CNGbf1JMTPk/ikLEb8dzHv/bEbp1Mrg5+kxYWABdK2QnbKGU8XEZHaT4GnHrm0cwTzHh7IJe3DKCx28q+58dz63p8cyNBD9srwDYGILsb+nt/NrUVERKqEAk89ExbgzftjevHCNV3wsdtYtvsQl772O9+s3Y+Jy6rVPiXjePYsNLUMERGpGgo89ZDFYuGWvs34+aGB9GgWRHZBEY/MWc+E2Ws5kqtlKQBoMcj4uft3UBAUEanzFHjqsehQP774Wz8eHdoWD6uFuRuTGD7tdxZtTzO7NPM17wdWO2QmwJE9ZlcjIiKVpMBTz3nYrDwwuA3f3H8BrRr5kZpdwJiZK3jmu03kF9bj6eueftC0t7Gv6ekiInWeAo8A0KVpA358YGDpE5o/XLaPK17/g3WJGabWZaqWmp4uIuIuFHiklI+njWev6sRHd/UhPNCL3em5XPfWUv6zYAdFxU6zy6t5pc/j+R2c9fDzi4i4EQUeOcnANo345eELubJrJMVOF68t2M51M5axOy3H7NJqVpOeYPeDvEOQutnsakREpBIUeKRcQb6evHHLefznpu4EeHuwPjGDK15fzMfL99Wf6esentC8v7GvcTwiInWaAo+c1tXdm/DLwxdyQeuG5DuK+ee3m3jws3XkFhSZXVrN0DgeERG3oMAjZ9Q4yIeP7uzLP6/ogIfVwg/rD3LNm0vqxy2uknE8+5ZCscPcWkREpMIUeOSsWK0W7h7Ykk/vOZ9GAV5sT8nh6jeWMH9zstmlVa/wzuDbEApz4MBqs6sREZEKUuCRc9I7OoS5DwygT3QI2QVF3PPRal6at5Vid12E1GqF6IHGvsbxiIjUWQo8cs7CAr35ZFxf7hrQAoA3F+5izMwVHMopMLmyaqJxPCIidZ4Cj1SI3Wbl6Ss78vrNPfCx21i8M50R/13sng8qLBnHk7gCCnPNrUVERCpEgUcq5apujfluwgW0CPXjYOZRbpyxjNl/JrjX1PWQltAgCpwOSFhmdjUiIlIBFQo8H3zwAXPnzi39/YknniAoKIj+/fuzb9++KitO6oa24QF8N+EChnUMp7DYyVPfbOTvX23gqMNN1uKyWI738mgcj4hInVShwPPCCy/g4+MDwLJly5g+fTovvfQSoaGhPPLII1VaoNQNgd523r69J3+/tD1WC3y+aj/Xz1hK4uE8s0urGhrHIyJSp1Uo8CQmJtK6dWsAvv32W6677jruuecepkyZwh9//FGlBUrdYbFYuG9QKz66qy8hfp5sOpDFiDcWs2h7mtmlVV6LC42fSRsg77C5tYiIyDmrUODx9/fn0KFDAMyfP5+hQ4cC4O3tTX5+ftVVJ3XSBa1D+eGBAXRr2oCMPAd3/G8F/43bgbMuT10PiIBG7QEX7FWoFxGpayoUeIYOHcrdd9/N3Xffzfbt27n88ssB2Lx5M9HR0VVZn9RRTYJ8+PzeftzStxkuF7wau51xH64iM78OP61Y43hEROqsCgWe6dOn069fP9LS0vjqq69o2LAhAKtXr+bmm2+u0gKl7vLysPHCNV146fqueHpYiduaylVvLCY+Kcvs0ipG43hEROosj4q8KCgoiDfeeOOk45MnT650QeJ+buwVRcfIQO79eDX7DuVxzZtLmHJtF67p0dTs0s5N8wvAYoVDOyHzADRoYnZFIiJylirUwzNv3jwWL15c+vv06dPp3r07t9xyC0eOHKmy4sR9dG7SgB8mDODCto046nDyyJz1PPv9ZoqKnWaXdvZ8gqBxD2NfvTwiInVKhQLP448/TlaWcVti48aNPProo1x++eXs2bOHmJiYKi1Q3Eewnyf/u6M3Dw5uA8CspXu59+M15BfWoef1aByPiEidVKHAs2fPHjp27AjAV199xZVXXskLL7zA9OnT+fnnn6u0QHEvNquFmKFtmXHbeXh5WFkQn8It7y3ncG6h2aWdnRPH8bjT06RFRNxchQKPp6cneXnGA+UWLFjAsGHDAAgJCSnt+RE5nUs7R/LJ3X1p4GNnbUIG1721lH2H6sA6VVF9weYF2UmQvsPsakRE5CxVKPAMGDCAmJgYnn/+eVasWMEVV1wBwPbt22natI4NRBXT9IoO4av7+tMkyIc96blc99ZSNuzPMLus07P7QLO+xr7G8YiI1BkVCjxvvPEGHh4efPnll7z11ls0aWLMVvn555+59NJLq7RAcW+tw/z55v7+dIwMJD2nkJveWc5v21LNLuv0SsfxLDS1DBEROXsVmpberFkzfvzxx5OOv/baa5UuSOqfsEBv5vztfO7/ZA1/7Ejn7g9WMeWaLtzYO8rs0srXchD8+rzxxGVnMVhtZlckIiJnUKHAA1BcXMy3335LfHw8AJ06deKqq67CZtO//OXcBXjbeX9Mb578egNfrznAE19t4GBmPg8NboPFYjG7vLIiu4NXIBzNhKT10OQ8sysSEZEzqNAtrZ07d9KhQwdGjx7N119/zddff81tt91Gp06d2LVrV1XXKPWEp4eVV2/oxviLWwEwbcEOJn69sfY9q8fmAdEDjH2N4xERqRMqFHgefPBBWrVqRWJiImvWrGHNmjUkJCTQokULHnzwwaquUeoRi8XC48Pb86+RnbFa4LOViYz7cBV5hUVml1aWnscjIlKnVCjwLFq0iJdeeomQkJDSYw0bNuTFF19k0aJz/w/A9OnTiY6Oxtvbm759+7JixYqzet1nn32GxWJh5MiR5/yeUrvddn5zZtzWE2+7ld+2pXHTO8tJzykwu6zjSp7Hk7AcimpRXSIiUq4KBR4vLy+ys7NPOp6Tk4Onp+c5XWvOnDnExMQwadIk1qxZQ7du3Rg+fDipqaefqbN3714ee+wxBg4ceE7vJ3XHsE4RfHL3+QT72tmwP5Pr3lrK3vRa8qyeRu3BPxyK8iHx7AK6iIiYp0KB58orr+See+7hzz//xOVy4XK5WL58Offeey9XXXXVOV1r6tSpjBs3jrFjx9KxY0dmzJiBr68vM2fOPOVriouLufXWW5k8eTItW7asyEeQOqJn82C+uq8/USE+7DuUx7VvLWVtQi1Yr81igRYXGvsaxyMiUutVaJbW66+/zpgxY+jXrx92ux0Ah8PB1VdfzbRp0876OoWFhaxevZqJEyeWHrNarQwZMoRly5ad8nXPPfccYWFh3HXXXfzxxx+nfY+CggIKCo7fcih5ErTD4cDhcJx1rWej5HpVfd36LirIi8/H9WHcR2vZdDCLm99dzrQbuzK4fRhgXrtbmg3AY+MXOHctpHjg32v0vWsDfd/NoXY3h9rdHCe2e2XbvkKBJygoiO+++46dO3eWTkvv0KEDrVu3PqfrpKenU1xcTHh4eJnj4eHhbN26tdzXLF68mPfff59169ad1XtMmTKFyZMnn3R8/vz5+Pr6nlO9Zys2NrZarlvfjW4K/8uzEp8B932ylhtaOrkg/Ph6VjXd7j6FxQwDOLCa+T98RZHNp0bfv7bQ990candzqN3NERsbW7qkVUWddeA50yrov/32W+n+1KlTK17RaWRnZ3P77bfz7rvvEhoaelavmThxYpnas7KyiIqKYtiwYQQGBlZpfQ6Hg9jYWIYOHVra8yVV68piJ09/v4Wv1hzk8902QqNaMv7CZixYsMCUdncd/C/WI3sY3t4fV5vhNfreZtP33Rxqd3Oo3c1xYrvn5+dX6lpnHXjWrl17Vuedy0PiQkNDsdlspKSklDmekpJCRETESefv2rWLvXv3MmLEiNJjTqfxjBYPDw+2bdtGq1atyrzGy8sLLy+vk65lt9ur7Utbndeu7+x2eOWG7jQJ9uP1uB28uWg3yVlHGeBpUru3vAhW78EjYQl0vLJm37uW0PfdHGp3c6jdzWG32ykqqtzjSc468JzYg1NVPD096dmzJ3FxcaVTy51OJ3FxcUyYMOGk89u3b8/GjRvLHPvnP/9JdnY2//nPf4iKqqVLEUiVslgsxAxtS2QDb/757Sa+XnuQrUFWhl3qpMb/PdTiIlg9S8/jERGp5Sq8tERViYmJYcyYMfTq1Ys+ffowbdo0cnNzGTt2LACjR4+mSZMmTJkyBW9vbzp37lzm9UFBQQAnHRf3d3OfZoQHenH/J2vYkgETPl3H26N74eVRg8ublMzUSt0MOWng36jm3ltERM5ahaalV6VRo0bxyiuv8Mwzz9C9e3fWrVvHvHnzSgcyJyQkkJSUZHKVUltd0j6ct2/tgd3iYuH2dMZ/sobCohpcisIvFMK7GPuani4iUmuZ3sMDMGHChHJvYQEsXLjwtK+dNWtW1RckdUr/Vg0Z197J+zvsLIhPZfzsNUy/5Tw8PWooz7e8CFI2GoGny/U1854iInJOTO/hEakK7YJcvHVrdzw9rMRuSWHC7DU4amrRUa2rJSJS6ynwiNsY2DqUd0f3wtPDyvwtKTwwe23NhJ7m/cHqARn74Mje6n8/ERE5Zwo84lYuatuIt2/viafNyrzNyTz4aQ2EHi9/aNLL2Fcvj4hIraTAI27n4nZhpaHn503JPPzZOoqqO/SUrJ6ugcsiIrWSAo+4pYvbh/HWbedht1mYuzGJh+dUc+gpGcez53dwuU5/roiI1DgFHnFbgzuE89atPbHbLPy4IYlHPl9ffaGnaW+w+0JuGqRuqZ73EBGRClPgEbc2pGM40285Dw+rhR/WH+TRL9ZT7KyGHhgPT2jWz9jXOB4RkVpHgUfc3rBOEbxxLPR8t+4gj1VX6NE4HhGRWkuBR+qFSztH8MYtPbBZLXyz9gCPV0foKRnHs3cJFFdukbtSGQnwyz/g2/vh0K6quaaISD2kwCP1xqWdI3njZiP0fL32AE98uaFqQ09EV/AJhsJsOLimctdKWg9f3gX/6Q7L3oB1n8D0vjD/n3A0s0rKFRGpTxR4pF65rEskr99khJ6v1uznya824Kyq0GO1QvRAY78i43hcLti5AD64Ct6+EDZ9Ca5io+eo1SXgdMDS/8Lr58Gq/4GzuGrqFhGpBxR4pN65omsk/7mpOzarhS9W72fi1xurLvRUZBxPUSGs+xTeugA+vs54rcUGna+HexbBmO/h9m/g1i8htC3kpcOPDxuhSAOkRUTOSq1YPFSkpl3ZtTFOFzz82VrmrErEYoEXrumC1Wqp3IVbDDJ+Jv4JhXng6Xvqc49mwuoPYPlbkH3QOGb3g55j4Pz7IKhZ2fPbDIWWg2Dl+7BwCqRsgg+vgvZXwtDnoGGrytUuIuLGFHik3rqqW2NcLhePzFnHZysTsVgs/N/IzpULPQ1bQWATyDoAicuNW1F/lXkA/nzLCDsFWcYx/3Doey/0GmuMAzoVmx3Ovxe63miEnpXvw9YfYfsvxvELHwfvBhWvX0TETemWltRrV3dvwtQbu2O1wKcrEnj6u024KvOkZIvl1Kunp2yGb+6F/3Q1xuIUZEFoO7jqDXh4IwyMOX3YOZFvCFz+Mty3FFoN1vgeEZEzUOCRem9kjya8ckM3LBb45M8E/m9ufOUueOI4HpcLdi80xua81R/WfwrOImg+AG6eA/cvh/NuBw+vir1XWHu4/WuN7xEROQPd0hIBrj2vKU4XPPbFet5bvIcmwT6MvaBFxS5W0sNzcJ0RPJI3GL9brNDhKuj/IDTtWSV1l9L4HhGR01IPj8gx1/dsyt8vbQ/Acz9uYd6mpIpdKDDS6G3BZYQdDx/oPQ4eWA03flD1YadEyfieB9dCn3uMmV5bf9Tze0REUOARKePei1py2/nNcLngoc/WsXrf4YpdaOBjEN4FBj0Fj2yGK16BkJZVW+ypaHyPiMhJFHhETmCxWHh2RCcGtw+joMjJ3R+sYk967rlfqNsouG8xDPo7+DWs+kLPxqnG97w3BDISzalJRMQkCjwif+Fhs/LfW3rQrWkDjuQ5uON/K0jPKTC7rIprM9To7bn038aU9YNr4J2LYM8fZlcmIlJjFHhEyuHr6cF7Y3oTFeLDvkN53PXBKvIL6/CtoJLxPfcuNtb8yjsEH15tPPSwMtPwRUTqCAUekVNoFODFrLF9CPK1sz4xgwc/W1v1K6zXtKBmcOcv0OVGY52ueU8azwZy5JtdmYhItVLgETmNVo38eW90Lzw9rMRuSWHyD5sr92DC2sDTF659B4a/YMzk2vAZzByucT0i4tYUeETOoFd0CNNGdcdigQ+X7ePdP3abXVLlWSzQb7yxKKlvQ0har3E9IuLWFHhEzsLlXSL5x+UdAHjhp638sP6gyRVVkZYXwT0LNa5HRNyeAo/IWbp7YEvGXhANwKOfr+fP3YfMLaiqBDWDu+ZD11Ea1yMibkuBR+Qc/POKjgzvFE5hsZNxH65iZ2q22SVVDbsPXPM2DJ9yfFzP+8MgI8HsykREqoQCj8g5sFkt/OemHpzXLIiso0WMmbmS1OyjZpdVNSwW6Hc/jP7WGNeTvAHeGQR7fje7MhGRSlPgETlH3nYb743pTYtQPw5k5HPnrJXkFhSZXVbVaXGhMa4nstuxcT0jYdmbGtcjInWaAo9IBYT4eTJrbG8a+nmy6UAWE2avoajYaXZZVafkeT0l43p+mQjf/E3jekSkzlLgEamg5g39eG9ML7ztVn7blsbT322q+8/oOVHJuJ5LXzw2rmeOxvWISJ2lwCNSCT2aBfP6TT2wWuDTFYlM/22n2SVVLYsFzr8PRn93fFzP2xfB7kVmVyYick4UeEQqaVinCJ69qhMAr8zfzjdr95tcUTVoMRDuWWSM68k/DB9dg/VPPa9HROoOBR6RKjC6XzR/u7AlAE98uYGlO9NNrqgaBEUdG9dzE7iKsS14mg5JX5pdlYjIWVHgEakif7+0PVd2jcRR7OJvH61ma3KW2SVVPbsPXDPDWIcLaJX6M2QlmVyUiMiZKfCIVBGr1cIrN3SjT4sQsguKGPu/lRzIcMNZTcfW4XJGnY/NVYR1+RtmVyQicka1IvBMnz6d6OhovL296du3LytWrDjluV9//TW9evUiKCgIPz8/unfvzkcffVSD1Yqcmrfdxju396RVIz+SMo9y8zvLOeiOoQdwDngUAOvaDyEn1eRqREROz/TAM2fOHGJiYpg0aRJr1qyhW7duDB8+nNTU8v8FGhISwj/+8Q+WLVvGhg0bGDt2LGPHjuWXX36p4cpFyhfk68lHd/WlWYgvCYfzuMlNQ4+rxSCO+LbEUpQPy6abXY6IyGmZHnimTp3KuHHjGDt2LB07dmTGjBn4+voyc+bMcs8fNGgQ11xzDR06dKBVq1Y89NBDdO3alcWLF9dw5SKn1jjIh8/uOd+9Q4/FwraIq439le9B3mFz6xEROQ1TA09hYSGrV69myJAhpcesVitDhgxh2bJlZ3y9y+UiLi6Obdu2ceGFF1ZnqSLnrD6EnpTA7rjCOkNhDvz5ttnliIickoeZb56enk5xcTHh4eFljoeHh7N169ZTvi4zM5MmTZpQUFCAzWbjzTffZOjQoeWeW1BQQEFBQenvWVnGzBmHw4HD4aiCT3FcyfWq+rpyerW53Rv5efDxnb249f2VJBzOY9Tby/jkrt5ENvA2u7RKczgcYLFQeP6DeH1/D64/36Ko99/AK8Ds0txabf6+uzO1uzlObPfKtr2pgaeiAgICWLduHTk5OcTFxRETE0PLli0ZNGjQSedOmTKFyZMnn3R8/vz5+Pr6Vkt9sbGx1XJdOb3a3O53tYD/5tlIPJLPtf9dxIROxQR7mV1V1ZiX4Mkl3o0JOHqQHZ88wY6IEWaXVC/U5u+7O1O7myM2Npa8vLxKXcPiMnHxn8LCQnx9ffnyyy8ZOXJk6fExY8aQkZHBd999d1bXufvuu0lMTCx34HJ5PTxRUVGkp6cTGBhY6c9wIofDQWxsLEOHDsVut1fpteXU6kq7H8zI57aZq0g8kk+zEB8+vrNu9/Sc2O6eW7/B4/v7cfk2pGj8GvD0M7s8t1VXvu/uRu1ujhPbPT8/n9DQUDIzMyv0329Te3g8PT3p2bMncXFxpYHH6XQSFxfHhAkTzvo6TqezTKg5kZeXF15eJ/9fabvdXm1f2uq8tpxabW/35o3sfPa3ftz8znISDudx+/9W8em482kc5GN2aZVit9vx6DYK/ngJy5G92Dd8Av3Gm12W26vt33d3pXY3h91up6ioqFLXMH2WVkxMDO+++y4ffPAB8fHx3HfffeTm5jJ27FgARo8ezcSJE0vPnzJlCrGxsezevZv4+HheffVVPvroI2677TazPoLIWWsS5MOnxwYy7zuUx83vuslAZpsHDIgx9pe8Do6j5tYjIvIXpo/hGTVqFGlpaTzzzDMkJyfTvXt35s2bVzqQOSEhAav1eC7Lzc3l/vvvZ//+/fj4+NC+fXs+/vhjRo0aZdZHEDknJaHn5neWl4aez+45n8gGdbunh243w6KXIGs/rP0I+owzuyIRkVKm9/AATJgwgX379lFQUMCff/5J3759S/+2cOFCZs2aVfr7v/71L3bs2EF+fj6HDx9m6dKlCjtS55SEnqgQH/YdMqasJ2XW8Z4eD08Y8LCxv+Q/UFRoajkiIieqFYFHpD5qEuTDZ/f0c6/Q0+M28A+HzETYMMfsakRESinwiJjI7UKP3Qf6P2Ds//EqFFdukKGISFVR4BExmduFnl53gk8IHNkDm782uxoREUCBR6RW+Gvoubkuhx5Pv+PT0n9/BZxOc+sREUGBR6TWODH07K3roafPOPBqAOnbYOsPZlcjIqLAI1KbNAny4dNx59f90OPdAPr+zdj//WUw74HuIiKAAo9IrdM02Nc9Qs/594GnPyRvhO0nL/siIlKTFHhEaqG/hp6b3llO4uHKLZxX43xDoPddxr56eUTEZAo8IrXUiaFn36E8rn1rKZsOZJpd1rnpNwE8vOHAKti90OxqRKQeU+ARqcWaBvvy5b39aR8RQFp2AaPeXsYfO9LMLuvs+YdBzzuM/d9fNrUUEanfFHhEarnwQG8+v7cf/Vo2JLewmLH/W8k3a/ebXdbZ6/8g2Dxh3xLYu8TsakSknlLgEakDAr3tzLqzN1d1a0yR08Ujc9YzY9EuXHVhXEyDJtD9VmP/j1fMrUVE6i0FHpE6wsvDxrRR3Rk3sAUAL/68lck/bKHYWQdCz4CHwWKDXb/C/tVmVyMi9ZACj0gdYrVa+McVHfnnFR0AmLV0Lw98uoajjmKTKzuD4GjoOsrY11geETGBAo9IHXT3wJa8fnMPPG1WftqYzOiZK8jMc5hd1ukNfBSwwPafIWmD2dWISD2jwCNSR13VrTGz7uxNgJcHK/Yc5oa3l9buBxSGtobO1xr7f7xqbi0iUu8o8IjUYf1bhfL5vf0ID/Rie0oO1765lG3J2WaXdWoDHzV+bvkO0raZW4uI1CsKPCJ1XIfIQL6+/wJah/mTlHmUG2Ys5c/dh8wuq3zhnaD9lYBLvTwiUqMUeETcQJMgH768tx+9mgeTdbSI299fwU8bk8wuq3wXPmb83PgFHN5tbi0iUm8o8Ii4iSBfTz6+uy/DO4VTWOxk/Ow1zFqyx+yyTta4B7QeCi4nLH7N7GpEpJ5Q4BFxI952G2/e2pPbz2+OywXP/rCFF3/eirO2PavnwseNn+s+hYxEc2sRkXpBgUfEzdisFp67uhOPD28HwIxFu3j0i/UUFjlNruwEzfpCiwvB6YAl/zG7GhGpBxR4RNyQxWJh/MWtefn6rtisFr5Ze4C7PlhJTkGR2aUdV9LLs+ZDyE42txYRcXsKPCJu7IZeUbw/phe+njb+2JHOqLeXkZp91OyyDNEDIaovFBfA0v+aXY2IuDkFHhE3N6hdGJ/dcz4N/TzZfDCLq99YUjumrVssx3t5Vs2E3FpQk4i4LQUekXqga9Mgvr6/Py1D/UjKPMrN7y5n6vxtFBWbPK6n9RCI7A6OPPhyLORnmFuPiLgtBR6ReqJ5Qz9+eGAAN/RsitMFr/+6k1HvLCfxcJ55RVkscOmLYPeDPYvg/aF6No+IVAsFHpF6xM/Lg5dv6MbrN/cgwMuD1fuOcPnrf/DjhoPmFdW8H9z5MwQ0hvTt8O5g2LvEvHpExC0p8IjUQ1d1a8xPDw2kR7Mgso8WMWH2Wp74cj15hSbN4orsBuN+NR5KmH8YPrwa1s02pxYRcUsKPCL1VFSIL5//rR8TLm6NxQKfr9rPla8vZtOBTHMKCoyEO36Cjlcbz+f59j5YMBmctej5QSJSZynwiNRjdpuVx4a3Y/bd5xMR6M3u9FyueXMJ7/2x25ynM3v6wvWzYOCx9bYWT4UvRkNhbs3XIiJuRYFHROjXqiE/PzSQYR3DcRS7+NfceMbOWkladkHNF2O1wuCnYeQMsNoh/gf43+WQVUsXQxWROkGBR0QACPbz5O3be/KvkZ3x8rCyaHsal/3ndxZtTzOnoO43w5jvwScEktbBu5fAwXU1895HM2HFu7D6A3DVsnXIRKRCFHhEpJTFYuG285vzwwMDaB8RQHpOIWNmruBfP26hoKi45gtq3h/GxUFoO8g+CP+7DOJ/rL73O7QLfnoCpnaEnx6DHx6ETV9V3/uJSI1R4BGRk7QND+Db8Rcwul9zAN5bvIfr3lrK7rScmi8mpCXcNR9aXmw8oHDObbB4WtX1vLhcsOs3mD0K/tsTVrwNhTngG2r8/afHIDulat5LREyjwCMi5fK223ju6s68O7oXwb52Nh3I4sr/LubzVYm4avo2j08Q3Pol9LoLcMGCSfDdBCgqrPg1Hfmweha82Q8+Ggnb5xnXbjMMbv8GYuIhoivkH4EfH9GtLZE6ToFHRE5raMdwfn7oQvq1bEheYTFPfLmBBz5dS1a+o2YLsXnAFa/CZS+BxQrrPoaProG8w+d2ncwDxnT3qR3gh4cgLd540nPvcTBhFdz6BbS6BDw84ZpjA6e3zYUNn1fP5xKRGqHAIyJnFNHAm4/v7svjw9ths1r4cUMSV725jB2ZlpotxGKBvn+DWz4HzwDYtxjeGwzpO8782sSV8MVYmNbFmO6efwSCmsGw/4OYLXDFKxDapuxrwjvBoL8b+z8/oZliInVYrQg806dPJzo6Gm9vb/r27cuKFStOee67777LwIEDCQ4OJjg4mCFDhpz2fBGpGjarhfEXt+bLe/sRFeLDgYyjvLHFxgOfra/59bjaDDXG9TRoZqy99d5g2L3w5POKHbDxS2OG1/tDYPPX4CqG5gNg1Mfw4DroP8G4ZXYqFzxiLHB6NAN+fFi3tkTqKNMDz5w5c4iJiWHSpEmsWbOGbt26MXz4cFJTU8s9f+HChdx888389ttvLFu2jKioKIYNG8aBAwdquHKR+qlHs2B+enAgt/RpigUX8zanMHjqIl7+ZSu5BTW4NEV4R2MGV9PexjTyj68zxuQA5B6C3182enO+ugsOrAabJ3S/Ff72B4ydCx1GgNV25vexecDIt4zXb58H6z+r1o8lItXD9MAzdepUxo0bx9ixY+nYsSMzZszA19eXmTNnlnv+J598wv3330/37t1p37497733Hk6nk7i4uBquXKT+CvC2M3lER57oWky/liEUFjmZ/tsuLn5lIV+u3l9zT2n2D4MxP0Ln68FZZIzJmXUlvNYRfv0XZCeBXxgMegoe2Qwj34TIruf+PuEdYdCTxv7Pf4csExdbFZEK8TDzzQsLC1m9ejUTJ04sPWa1WhkyZAjLli07q2vk5eXhcDgICQkp9+8FBQUUFBx/WmxWVhYADocDh6NqB12WXK+qryunp3Y3h8PhoLEfvHd1VxbtzGDKvG0kHsnnsS/W8+HSPfzz8vb0aBZUA5XY4Kq3sIa0wvb7v2HvHwA4I7rh7PM3XB2uBg+vkqIr/jZ97se25QesSWtxfvcAxaM+NcYU1TB9382hdjfHie1e2ba3uGp8fulxBw8epEmTJixdupR+/fqVHn/iiSdYtGgRf/755xmvcf/99/PLL7+wefNmvL29T/r7s88+y+TJk086Pnv2bHx9fSv3AUSkVJETFiVZ+GW/lQKnEQR6hjq5qpmTIK+aqSEiYzVhWRvYH3IBh/3aVHkgCcg/wEXbnsHmcrC22d0kNLywSq8vIqeWl5fHLbfcQmZmJoGBgef8elN7eCrrxRdf5LPPPmPhwoXlhh2AiRMnEhMTU/p7VlZW6bifijTY6TgcDmJjYxk6dCh2u71Kry2npnY3R3ntfhXwZHYBUxfs5Ku1B1idbmVzpgf3DGzB3RdE4+N5FmNmKuVyAJpW51ssy4dfn6N7yhw6X/0gBDauznc7ib7v5lC7m+PEds/Pz6/UtUwNPKGhodhsNlJSyj7FNCUlhYiIiNO+9pVXXuHFF19kwYIFdO166nvyXl5eeHmd/H8v7XZ7tX1pq/Pacmpqd3P8td0bh9h55cbujOnfgud+3MzKvUd4/dddfLn6AE9e3oERXSOxmHArqMoMeBi2/4xl/0rsPz0Ct31lyq0tfd/NoXY3h91up6iocpMiTB207OnpSc+ePcsMOC4ZgHziLa6/eumll3j++eeZN28evXr1qolSReQcdWnagM//1o//3tyDJkE+HMw8yoOfruWGGcvYuD/T7PIqzmqDq98EmxfsioM1H5pdkYicBdNnacXExPDuu+/ywQcfEB8fz3333Udubi5jx44FYPTo0WUGNf/73//m6aefZubMmURHR5OcnExycjI5OSas8SMip2WxWBjRrTFxj15EzNC2+NhtrNp3hKumL+bxL9aTmn3U7BIrplFbGPy0sf/LPyAj0dx6ROSMTA88o0aN4pVXXuGZZ56he/furFu3jnnz5hEeHg5AQkICSUnHn2761ltvUVhYyPXXX09kZGTp9sorr5j1EUTkDLztNh4c3IZfH7uIkd0b43LBF6v3c/HLC3lr4S5zVmKvrPPvh6i+UJgN3z+gBxKK1HK1YtDyhAkTmDBhQrl/W7hwYZnf9+7dW/0FiUi1iGzgw7SbejC6fzSTf9jC+sQM/j1vK5+uSGDCxa25ukdjvDyqe2BzFSm5tTXjAtj9m/HQw15jza5KRE7B9B4eEal/zmsWzDf39efVG7oRFuBFwuE8nvhqAwP+/RvTf9tJRl4lVkGvSaGtYfAkY3/+P+HIPnPrEZFTUuAREVNYrRau69mU3x4bxFOXtyeygTdp2QW8/Ms2+k35lUnfbWLfoVyzyzyzvvdCs35QmAPfTwCn0+yKRKQcCjwiYio/Lw/uubAVvz9xMdNGdadjZCD5jmI+WLaPi19ZyH0fr2ZNwhGzyzw1qxWung4ePrDnd1hd/rI4ImIuBR4RqRXsNisjezRh7oMDmH13Xwa1a4TTBT9vSubaN5dy3VtLmbcpmeKaWqfrXDRsBUOeNfbnPwNH9ppZjYiUQ4FHRGoVi8VC/9ahzBrbh/mPXMiNvZriabOyet8R7v14NYNfXchHy/aSX1jLZnb1uQeaXwCOXPhOt7ZEahsFHhGptdqGB/DS9d1Y/PeLGX9xKxr42Nl7KI+nv9tM/xfjmDp/G2nZBWe+UE0oubVl9zUWMF35ntkVicgJFHhEpNYLC/Tm8eHtWTbxEiZf1YmoEB+O5Dl4/dedXPDvX3nyqw3sTM02u0wIaQFDnzP2F0yCw7vNrUdESinwiEid4evpwZj+0Sx87GLeuvU8ukcFUVjk5LOViQyZ+jt3zlrJ0p3pOM0c59PrLogeCI483doSqUVqxYMHRUTOhc1q4bIukVzaOYLV+47w7h+7mb8lhV+3pvLr1lQaN/Dmym6NGdG1MZ2bBNbsYqVWK1z9BrzZH/YtgRXvwPn31tz7i0i5FHhEpM6yWCz0ig6hV3QIe9JzeX/xbr5be5CDmUd55/fdvPP7blqE+jGiayQjujWmTXhAzRQWHA3Dnoe5MbDgWWgz1JjJJSKm0S0tEXELLUL9+NfILqz85xBm3NaTK7pG4m23sic9l9d/3cnQ137n0mm/M/23nSQcyqv+gnrdCS0ugqJ8+PZ+cNayWWUi9Yx6eETErXjbbVzaOYJLO0eQU1BEXHwKP6w/yKLtaWxNzmZr8jZe/mUb3aOCGNGtMVd2jSQ80LvqC7FYjt3a6geJy+HXf8GFj4Onb9W/l4ickQKPiLgtfy8Pru7ehKu7NyEjr5BfNifzw/oklu5KZ11iBusSM/jX3C30bRHCiG6NuaxzJCF+nlVXQFAzGPYv+PFhWDwVVr4P3UZBzzsgvFPVvY+InJECj4jUC0G+nozq3YxRvZuRmn2Unzcm8/36g6zed4Tluw+zfPdhJn23mQFtQhnRtTHDOoUT4G2v/Bv3vAOKCmD5m5CxzxjEvOIdaNobeo6FTteo10ekBijwiEi9ExbgzZj+0YzpH83+I3nM3ZDE9+sPsvlgFgu3pbFwWxqe31i5qG0jhnYIZ1D7RoQFVPC2l8VizNLqcw/s/g1Wz4JtP8H+lcY2byJ0vdEIRhGdq/JjisgJFHhEpF5rGuzL3y5qxd8uasWutBx+XJ/E9+sPsCstl9gtKcRuSQGgW1QQg9uHcUn7MDo1rsBUd6sVWg82tuwUWPcJrPnAWHdr5bvG1qSXEXw6XwueflX+WUXqMwUeEZFjWjXy56EhbXhwcGvik7KJ3ZJC3NYUNuzPZH1iBusTM5gau52IQG8u6RDGkA5h9G8Virfddm5vFBAOA2PggodhzyJY/T/YOhcOrDK2X546odenS3V8VJF6R4FHROQvLBYLHRsH0rFxIA8NaUNq1lF+3ZpK3NZUFu9IJznrKLP/TGD2nwl4260MaB3KJe3DuaR9GBENzuHWl9UKrS42tpxUo9dn9axjvT7vGVuTnkbw6XQtePlX0ycWcX8KPCIiZxAW6M1NfZpxU59mHHUUs2z3IX6NTyUuPoWDmUdZEJ/KgvhUADo3CeSS9uEM6RBG58YNsFrP8taXfxgMeAT6P3Ss12cWbP0RDqw2tnnHen263VZ9H1TEjSnwiIicA2+7jYvbhXFxuzCeu7oTW5OziYtPIW5rKusSM9h0IItNB7J4PW4HjQK8uKRdGIM7hDGgTSi+nmfxr9yTen1mH+v12QOr3se+6n2GezTAlvUhNO4Okd2MLaiZMUBaRMqlwCMiUkEWi4UOkYF0iAxkwiVtSMsuYOE2Yz2v37enkZZdwJxVicxZlYiXh5WBbRoxvFM4gzuEn93zfvzDYMDD0P9B2Ps7rJ6FK/5HvIsyYdcCYyvhHXQ8/JRsIa2MACUiCjwiIlWlUYAXN/SK4oZeURQUFbNiz2Hi4lNZEJ/C/iP5LIhPYUF8ClYL9GkRwrCOEQzrFE7T4DM8h8dqhZaDoOUgivIyWfbte1zQwh9b6kZIWg+p8XA0w7gVtmfR8dd5+huDnk8MQaHtwKZ/9Uv9o2+9iEg18PKwMbBNIwa2acSkER2JT8pm/pZk5m9OYUtSVunDDp/7cQudGgcyrGMEwzuH0y484PRT3u2+HPFrjbPX5djsxx6MWFRghJ6k9caWvAGSN0FhDiQsM7YSNi/jKc8nhqDwTuDhVb0NImIyBR4RkWp24qyvh4e0JfFwHvO3pPDL5mRW7T3M5oNZbD6YxWsLttMsxJfhncIZ1imC85oFYzubQc8eXsZ4nsbdjx8rLoJDO46HoKT1kLQBCrPh4BpjK2H1gLAOENndCECNexghyO5TxS0hYh4FHhGRGhYV4stdA1pw14AWHMopIC4+lflbkvl9RzoJh/N49489vPvHHkL9PRnSIZxhncLP/Xk/tmMhJqwDdLvJOOZ0GoOfSwPQOuNn/hFI3mhsaz8yzrXYoFH7YwGou/EzooseiCh1lgKPiIiJGvp7cWPvKG7sHUVuQRG/b09j/pYU4uJTSM8p5LOViXy2MhE/TxuD2oUxuH0oeY4KvpnVCg1bGVvna41jLhdkJsLBdcdD0MF1kJcOqZuNbf3sYxewQGjbE2aHdTdCkHdgJVtBpPop8IiI1BJ+Xh5c1iWSy7pE4ih28ufuw/yyOZn5W5JJySpg7sYk5m5MAjyYuXcxvaMb0is6mD4tQmgW4nvuy12AMZU9qJmxdbzKOOZyQdbBsr1AB9dBTjKkbzO2DXOOX6NhawjvDMHNj13r2M8GUVoYVWoNBR4RkVrIbrMyoE0oA9qEMvmqTmw4kMn8zcks2JLC9tQcdqfnsTs9jzmrEgFjhljv6GB6NQ+hT4sQ2kcE4GGr4JR0iwUaNDG29pcfP56dfPx2WEmPUNZ+OLTT2Mrj1+h4+CkJViWBKChKt8ikxijwiIjUclarhe5RQXSPCuKRwa344rufaNi+N2sSM1m19wgb9meQll3ATxuT+WljMgB+njbOa24EoN4tgukeFXR2Dz48nYAIY2s7/PixnDRIXg+pW41bYxkJx7eCLMhNM7YDq8u/pm/oCUEoyghDoW2g8Xm6VSZVSoFHRKSO8bPDJe0aMbxzYwCOOorZsD+TlXsPs3LvYVbvPUJ2QRF/7Ejnjx3pAHhYLXRq0oDezYPp3SKEXs2DaehfBVPR/RtB6yHG9lf5GWUDUJltnxGI8tKN7cRZYwBYjEHTTXsd23obv1vPcaFWkWMUeERE6jhvu40+LYxbWQDFThfbU7JZtfcwK/YeYeWewyRnHS1d8f29xXsAaBnqR4fIQNqE+9MuPIA24QFEN/St+K2wv/IJMrbIruX/vSQQndgzdGQfpGw09tPija1k5pinvzFlvmlvIwQ16WWsPC9yFhR4RETcjM16fMmL2/tF43K5OJCRz6q9R1ix9zCr9h5me0oOu9Nz2Z2eCxuPv9bTZqVVmD9tw/1pGx5A2/AA2oUH0DTY5+wXQj1bpwtEOamwfxXsXwkHVsGBNcaDFPf+YWwlGjQ73gPUtBdEdAX7OaxYL/WGAo+IiJuzWCw0DfalabAvI3s0ASAjr5B1iRnsSMlhW0o2O1Ky2Z6SQ76jmPikLOKTsspcw8duo024P23CAmgX4U+bY0EosoF3xWaHnYl/mDFgumTQtLMY0rYeD0H7Vxm/ZyYY2+avjfOsdmOqfNPexhbeCUJa6CGKosAjIlIfBfl6MqhdGIPahZUeczqNnqBtydlsT81me7IRgnamGUFow/5MNuzPLHOdAC8P45ZYRCCdmwTSuXED2kUEnNtDEs+G1WaEl/BO0HOMcexoljH2Z/+q40GoZDzQwTWw4u1jL7ZAg6YQ0vLYc4haGwurNmxtTKW32au2VqmVFHhERAQwZoNFhfgSFeLLkI7Hx8YUFTtJOJzH9mO9QCU9QrvTcskuKGJNQgZrEjJKz/ewWmgTHkCXJoF0btKAzk0a0CEiEB/PKg5B3oGli6oCxvODMvYdD0AHVkHadijINMYJZSaWXVwVjCdKBzUzwk9pGGpp/GzQVIOk3YgCj4iInJaHzUrLRv60bOTPpZ2PHy8scrL3UC7bkrOJT8pi08EsNh3I5HBuYeltsc9X7QeMcUWtG/nTqUkgXY6FoI6Rgfh5VeF/hiwWCI42ti7XG8dcLsg7BId2Gc8KOrzr2P4uY9+RZyy3cWQP7Iwtez2bJwS3gIatsQY1o/3BA1h/3wgensZ7WWxgsRqhqMy+9S/7trLH7T5GD1NIC/Uu1SAFHhERqRBPD2vpwOYR3Ywp8i6Xi6TMo2w8kMnmA5lsPJDJxgNZpOcUsC0lm20p2Xy95gBgZIaWoX6lAahzkwZ0ahxIgHcVhgCLBfxCja1Z37J/c7kgO+l4+Dm0Ew7tNn4e2QPFhaVPlrYB7QBSqq40rB5G8GnUFkLbQaN2xtIdoW30QMZqoMAjIiJVxmKx0DjIh8ZBPgzvFFF6PCXrKBv3Z7LpYCabDmSy6UAWyVlH2ZWWy660XL5dd7D03GYhvrRq5EfrMH9aNfKnVZg/rRv5E+znWdXFQmBjY2sxsOzfnMWQeewp0od3U3xoF/t27aB5syhsFhe4nMZirK7iY/vFf9l3nvp4QbYRshy5x5fq4Iey79+g2QlB6IRA5BtStW1Qj5geeKZPn87LL79McnIy3bp147///S99+vQp99zNmzfzzDPPsHr1avbt28drr73Gww8/XLMFi4jIOQsP9Ca8o3eZsUFp2QVGACoNQlkcyMgn4XAeCYfz+G1bWplrhPh5lg1CjfxpHeZP4yAfbFU9Zd5qMwY0BzcHBuN0ONhY+BNRl12OzV4FPVBOJ2QdMMJO2vayP/MOHZ99tnNB2df5NSobgkLbGGOPGkRpvNEZmBp45syZQ0xMDDNmzKBv375MmzaN4cOHs23bNsLCwk46Py8vj5YtW3LDDTfwyCOPmFCxiIhUlUYBXlzcLoyLT5gpdji3kO0p2exKy2Fnao7RA5Saw4GMfA7nFnI4t5CVe4+UuY6Xh5UWoSf3CLVs5Ff1s8WqitV6bCmNqJOfUp176FgA2gbp24//zEw8vlTHvsVlX1M63qjV8dloIa2MnwGNjfer50wNPFOnTmXcuHGMHTsWgBkzZjB37lxmzpzJk08+edL5vXv3pnfv3gDl/l1EROq2ED9Pzm/ZkPNbNixzPK+wiN1puexKy2HXsSC0MzWHPem5FBQ52Zqczdbk7DKvsVggKtjXeHhixPEHKbZs5IeXRy0NQgB+DcGvPzTvX/Z4QQ4c2nFCj9A2SN9x0nijk3j4HAtBLY+HoJJp+f5hRkPVA6YFnsLCQlavXs3EiRNLj1mtVoYMGcKyZcuq7H0KCgooKCgo/T0ry3iYlsPhwOFwVNn7lFzzxJ9SM9Tu5lC7m6O+trvdAu3CfGkX5gudjvcIFTtd7M/IZ1da7rFAZDw9eldaDpn5RaW3xxbEHx9tbLNaiG7oS9sw/2MPU/SnbZg/zUJ8TrmsRq1od6sXNOpsbCdyFkPWASyHd2M5vBsO78JyeBeWI7shIwFLUT6kbja2v3B5+kFwS1whLXF5B2FxFoHTAcUOcBYZW+m+A4qNYxan4y9/K3ueq/F5FN/8RaU/8ontXtm2Ny3wpKenU1xcTHh42XVQwsPD2bp1a5W9z5QpU5g8efJJx+fPn4+vr2+Vvc+JYmNjz3ySVDm1uznU7uZQu5+sMdDYCwY2AVdjyCmC5DwLSXmQlG8hKc9Cch7kF1M6WPrnzceDkIfFRbgPRPq6iPB1EekLkT4ugr2gZIhQ7W/3CGMLvAACwdKsCN/CQ/gdTca/IBm/gpTSn76F6VgKcyFlI5aUjWe88rk4kpzAHz/9VGXXi42NJS8vr1LXMH3QcnWbOHEiMTExpb9nZWURFRXFsGHDCAwMrNL3cjgcxMbGMnToUOxVMahNzora3Rxqd3Oo3SvH5XKRnFXAztQctqfmsD0lhx2pxnihfIeTA3lwIK/sLR4/TxstQ33xdmTSv3MrWocH0jLUl+YhvnjV1jFCZ6GoqAAy9hm9QYd3G88kstqNwc82O1jtuKwex/Y9jK103172uNWOy1Zy3IMAux+XBzaudI0nft/z8/MrdS3TAk9oaCg2m42UlLIPNUhJSSEiIuIUrzp3Xl5eeHl5nXTcbrdX278sqvPacmpqd3Oo3c2hdq+4ZqGeNAsN4JKOx485nS72H8lnW0r2sSdKZ7Mt2XiadG5hMRsPZgNWVv62p/Q1Vgs0CfahZagxQLplI39ahRo/wwO9qmeNsapkt4NPJ4jsZHYlZ2S32ykqKqrUNUwLPJ6envTs2ZO4uDhGjhwJgNPpJC4ujgkTJphVloiI1ENWq4VmDX1p1tCXoSdMnXcUO9l3KJctBzL4Zela7CFN2XM4n91pOWQfLSLxcD6Jh/NZtL3sFHo/TxstGvmVCUMtQ/1o2cgPX0+3v7lSK5na6jExMYwZM4ZevXrRp08fpk2bRm5ubumsrdGjR9OkSROmTJkCGAOdt2zZUrp/4MAB1q1bh7+/P61btzbtc4iIiHuy26y0DgugebA3rgQXl1/eBbvdjsvlIj2nkN1pOexOzzV+HhswnXA4j9zCYjYdyGLTgayTrhkR6E3jIG8ig3xo3MCbyAY+xu8NfIgM8ibUzwtrVT9XSMwNPKNGjSItLY1nnnmG5ORkunfvzrx580oHMickJGA94dkBBw8epEePHqW/v/LKK7zyyitcdNFFLFy4sKbLFxGRespisdAowItGAV70/csU+sIiY7HV8sLQ4dxCkrOOkpx1FE5YcPVEnjYrEQ28iWzgTeMgHyIbnByOGvjYa/8ts1rG9H61CRMmnPIW1l9DTHR0NC6XqwaqEhERqRhPDyutw4ynQP9VRl4hew/lkZSRz8HMoyRl5JOUeZSDmfkkZRwlNfsohcdWp084fOpZST52G5FB3jQN9i29VVZy+ywi0Fs9ROUwPfCIiIjUF0G+nnT39aR7VFC5f3cUO0nNLigTiA6W7B8LRYdyC8l3FBu9Rmm5/P6X8UM+dhstSkJQI39aHQtDLRr54V+Vq9PXMfX3k4uIiNQydpuVJkE+NAnyOeU5Rx3FJB/rFUo8nHfCAxdzSDiUR76jmC1JWWxJOnn8UFiAV5lB1K2OLcHRNNi36tcjq2UUeEREROoQb7uN6FA/okP9oFXZvzmKnaUhaHd6Tmkv0O70HNJzCknNLiA1u4Dluw+XeZ2nzUqovyc+njZ8PG342j2MfbsN32PHju974GO34utZzjnHXuvv7UFIVa9uX0kKPCIiIm7CbrMavTeN/IGyKxlk5jtOGEB9PAztOZRLYZGTg5lHq6yOrk0b8P2EAVV2vaqgwCMiIlIPNPCx06NZMD2aBZc5Xux0cTAjnyN5heQVFpNfWEy+o/jYftEJ+8Vl9vOO/c3YLy6z7+tZ+55ArcAjIiJSj9msFqJCfIkKqbr1JWvjjOryl4UVERERqaDa+IwgBR4RERFxewo8IiIi4vYUeERERMTtKfCIiIiI21PgEREREbenwCMiIiJuT4FHRERE3J4Cj4iIiLg9BR4RERFxewo8IiIi4vYUeERERMTtKfCIiIiI21PgEREREbfnYXYBNa1kyfqsrKwqv7bD4SAvL4+srCzsdnuVX1/Kp3Y3h9rdHGp3c6jdzXFiu+fn5wPH/zt+rupd4MnOzgYgKirK5EpERETkXGVnZ9OgQYNzfp3FVdGoVEc5nU4OHjxIQEAAFoulSq+dlZVFVFQUiYmJBAYGVum15dTU7uZQu5tD7W4Otbs5Tmz3gIAAsrOzady4MVbruY/IqXc9PFarlaZNm1brewQGBup/ECZQu5tD7W4Otbs51O7mKGn3ivTslNCgZREREXF7CjwiIiLi9hR4qpCXlxeTJk3Cy8vL7FLqFbW7OdTu5lC7m0Ptbo6qbPd6N2hZRERE6h/18IiIiIjbU+ARERERt6fAIyIiIm5PgUdERETcngJPFZk+fTrR0dF4e3vTt29fVqxYYXZJbu3ZZ5/FYrGU2dq3b292WW7n999/Z8SIETRu3BiLxcK3335b5u8ul4tnnnmGyMhIfHx8GDJkCDt27DCnWDdypna/4447Tvr+X3rppeYU60amTJlC7969CQgIICwsjJEjR7Jt27Yy5xw9epTx48fTsGFD/P39ue6660hJSTGpYvdwNu0+aNCgk77z99577zm9jwJPFZgzZw4xMTFMmjSJNWvW0K1bN4YPH05qaqrZpbm1Tp06kZSUVLotXrzY7JLcTm5uLt26dWP69Onl/v2ll17i9ddfZ8aMGfz555/4+fkxfPhwjh49WsOVupcztTvApZdeWub7/+mnn9Zghe5p0aJFjB8/nuXLlxMbG4vD4WDYsGHk5uaWnvPII4/www8/8MUXX7Bo0SIOHjzItddea2LVdd/ZtDvAuHHjynznX3rppXN7I5dUWp8+fVzjx48v/b24uNjVuHFj15QpU0ysyr1NmjTJ1a1bN7PLqFcA1zfffFP6u9PpdEVERLhefvnl0mMZGRkuLy8v16effmpChe7pr+3ucrlcY8aMcV199dWm1FOfpKamugDXokWLXC6X8f222+2uL774ovSc+Ph4F+BatmyZWWW6nb+2u8vlcl100UWuhx56qFLXVQ9PJRUWFrJ69WqGDBlSesxqtTJkyBCWLVtmYmXub8eOHTRu3JiWLVty6623kpCQYHZJ9cqePXtITk4u891v0KABffv21Xe/BixcuJCwsDDatWvHfffdx6FDh8wuye1kZmYCEBISAsDq1atxOBxlvvPt27enWbNm+s5Xob+2e4lPPvmE0NBQOnfuzMSJE8nLyzun69a7xUOrWnp6OsXFxYSHh5c5Hh4eztatW02qyv317duXWbNm0a5dO5KSkpg8eTIDBw5k06ZNBAQEmF1evZCcnAxQ7ne/5G9SPS699FKuvfZaWrRowa5du3jqqae47LLLWLZsGTabzezy3ILT6eThhx/mggsuoHPnzoDxnff09CQoKKjMufrOV53y2h3glltuoXnz5jRu3JgNGzbw97//nW3btvH111+f9bUVeKROuuyyy0r3u3btSt++fWnevDmff/45d911l4mViVS/m266qXS/S5cudO3alVatWrFw4UIGDx5sYmXuY/z48WzatEljA2vYqdr9nnvuKd3v0qULkZGRDB48mF27dtGqVauzurZuaVVSaGgoNpvtpFH6KSkpREREmFRV/RMUFETbtm3ZuXOn2aXUGyXfb333zdeyZUtCQ0P1/a8iEyZM4Mcff+S3336jadOmpccjIiIoLCwkIyOjzPn6zleNU7V7efr27QtwTt95BZ5K8vT0pGfPnsTFxZUeczqdxMXF0a9fPxMrq19ycnLYtWsXkZGRZpdSb7Ro0YKIiIgy3/2srCz+/PNPffdr2P79+zl06JC+/5XkcrmYMGEC33zzDb/++istWrQo8/eePXtit9vLfOe3bdtGQkKCvvOVcKZ2L8+6desAzuk7r1taVSAmJoYxY8bQq1cv+vTpw7Rp08jNzWXs2LFml+a2HnvsMUaMGEHz5s05ePAgkyZNwmazcfPNN5tdmlvJyckp8/+g9uzZw7p16wgJCaFZs2Y8/PDD/Otf/6JNmza0aNGCp59+msaNGzNy5EjzinYDp2v3kJAQJk+ezHXXXUdERAS7du3iiSeeoHXr1gwfPtzEquu+8ePHM3v2bL777jsCAgJKx+U0aNAAHx8fGjRowF133UVMTAwhISEEBgbywAMP0K9fP84//3yTq6+7ztTuu3btYvbs2Vx++eU0bNiQDRs28Mgjj3DhhRfStWvXs3+jSs3xklL//e9/Xc2aNXN5enq6+vTp41q+fLnZJbm1UaNGuSIjI12enp6uJk2auEaNGuXauXOn2WW5nd9++80FnLSNGTPG5XIZU9OffvppV3h4uMvLy8s1ePBg17Zt28wt2g2crt3z8vJcw4YNczVq1Mhlt9tdzZs3d40bN86VnJxsdtl1XnltDrj+97//lZ6Tn5/vuv/++13BwcEuX19f1zXXXONKSkoyr2g3cKZ2T0hIcF144YWukJAQl5eXl6t169auxx9/3JWZmXlO72M59mYiIiIibktjeERERMTtKfCIiIiI21PgEREREbenwCMiIiJuT4FHRERE3J4Cj4iIiLg9BR4RERFxewo8IlLvLVy4EIvFctIaSSLiPhR4RERExO0p8IiIiIjbU+AREdM5nU6mTJlCixYt8PHxoVu3bnz55ZfA8dtNc+fOpWvXrnh7e3P++eezadOmMtf46quv6NSpE15eXkRHR/Pqq6+W+XtBQQF///vfiYqKwsvLi9atW/P++++XOWf16tX06tULX19f+vfvz7Zt26r3g4tIjVHgERHTTZkyhQ8//JAZM2awefNmHnnkEW677TYWLVpUes7jjz/Oq6++ysqVK2nUqBEjRozA4XAARlC58cYbuemmm9i4cSPPPvssTz/9NLNmzSp9/ejRo/n00095/fXXiY+P5+2338bf379MHf/4xz949dVXWbVqFR4eHtx555018vlFpPpp8VARMVVBQQEhISEsWLCAfv36lR6/++67ycvL45577uHiiy/ms88+Y9SoUQAcPnyYpk2bMmvWLG688UZuvfVW0tLSmD9/funrn3jiCebOncvmzZvZvn077dq1IzY2liFDhpxUw8KFC7n44otZsGABgwcPBuCnn37iiiuuID8/H29v72puBRGpburhERFT7dy5k7y8PIYOHYq/v3/p9uGHH7Jr167S804MQyEhIbRr1474+HgA4uPjueCCC8pc94ILLmDHjh0UFxezbt06bDYbF1100Wlr6dq1a+l+ZGQkAKmpqZX+jCJiPg+zCxCR+i0nJweAuXPn0qRJkzJ/8/LyKhN6KsrHx+eszrPb7aX7FosFMMYXiUjdpx4eETFVx44d8fLyIiEhgdatW5fZoqKiSs9bvnx56f6RI0fYvn07HTp0AKBDhw4sWbKkzHWXLFlC27ZtsdlsdOnSBafTWWZMkIjUL+rhERFTBQQE8Nhjj/HII4/gdDoZMGAAmZmZLFmyhMDAQJo3bw7Ac889R8OGDQkPD+cf//gHoaGhjBw5EoBHH32U3r178/zzzzNq1CiWLVvGG2+8wZtvvglAdHQ0Y8aM4c477+T111+nW7du7Nu3j9TUVG688UazPrqI1CAFHhEx3fPPP0+jRo2YMmUKu3fvJigoiPPOO4+nnnqq9JbSiy++yEMPPcSOHTvo3r07P/zwA56engCcd955fP755zzzzDM8//zzREZG8txzz3HHHXeUvsdbb73FU089xf3338+hQ4do1qwZTz31lBkfV0RMoFlaIlKrlcygOnLkCEFBQWaXIyJ1lMbwiIiIiNtT4BERERG3p1taIiIi4vbUwyMiIiJuT4FHRERE3J4Cj4iIiLg9BR4RERFxewo8IiIi4vYUeERERMTtKfCIiIiI21PgEREREbenwCMiIiJu7/8BiJlk8seM6jMAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "aV7PKoYdSfYl",
        "outputId": "b4a84ee5-ecf0-4406-cb5a-0d5232baa0bd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history, 'accuracy', 'val_accuracy', 'Accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yxITITKnfvcN",
        "outputId": "285e66d4-d56b-44af-f0e0-3efff3313d0f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history, 'precision', 'val_precision', 'Precision')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Kt6Z8B_sfvcN",
        "outputId": "6e904d9d-5b04-4747-f72d-82a79ff6d8ae"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history, 'recall', 'val_recall', 'Recall')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QJKT4mXnDUIq",
        "outputId": "371443d2-59e2-4bb8-dffd-63b35cb4e297"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m190/190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 263ms/step - accuracy: 0.4084 - loss: 0.6931 - precision: 0.5916 - recall: 1.0000\n"
          ]
        }
      ],
      "source": [
        "# before training\n",
        "print(loss0, accuracy0, precision0, recall0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "outputId": "4a1bc771-c602-4e8b-daf1-65288b8d2392",
        "id": "uFYB1umjVsKm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m190/190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 257ms/step - accuracy: 0.9606 - loss: 0.1174 - precision: 0.9585 - recall: 0.9785\n"
          ]
        }
      ],
      "source": [
        "# after 25 epochs\n",
        "loss_test, accuracy_test, precision_test, recall_test = detector.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxUpIYQVDUIq"
      },
      "source": [
        "## TRAINING ANOTHER 25 EPOCHS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VWu8sNQV-fvB"
      },
      "outputs": [],
      "source": [
        "early_stopping = tfk.callbacks.EarlyStopping(\n",
        "    monitor=\"val_loss\",\n",
        "    min_delta=1e-4,\n",
        "    patience=5,\n",
        "    verbose=1,\n",
        "    mode=\"auto\",\n",
        "    baseline=None,\n",
        "    restore_best_weights=True,\n",
        "    start_from_epoch=5,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6EQW-R8l-fvB",
        "outputId": "51d7b061-6883-4b3d-f4b7-b939102dd126"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 26/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m388s\u001b[0m 404ms/step - accuracy: 0.9708 - loss: 0.0747 - precision: 0.9739 - recall: 0.9824 - val_accuracy: 0.9682 - val_loss: 0.0926 - val_precision: 0.9702 - val_recall: 0.9749\n",
            "Epoch 27/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 303ms/step - accuracy: 0.9760 - loss: 0.0660 - precision: 0.9779 - recall: 0.9836 - val_accuracy: 0.9685 - val_loss: 0.0910 - val_precision: 0.9690 - val_recall: 0.9785\n",
            "Epoch 28/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m328s\u001b[0m 309ms/step - accuracy: 0.9766 - loss: 0.0642 - precision: 0.9781 - recall: 0.9850 - val_accuracy: 0.9687 - val_loss: 0.0894 - val_precision: 0.9785 - val_recall: 0.9711\n",
            "Epoch 29/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m300s\u001b[0m 339ms/step - accuracy: 0.9778 - loss: 0.0623 - precision: 0.9793 - recall: 0.9854 - val_accuracy: 0.9696 - val_loss: 0.0852 - val_precision: 0.9765 - val_recall: 0.9745\n",
            "Epoch 30/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m321s\u001b[0m 338ms/step - accuracy: 0.9794 - loss: 0.0572 - precision: 0.9805 - recall: 0.9862 - val_accuracy: 0.9694 - val_loss: 0.0842 - val_precision: 0.9803 - val_recall: 0.9730\n",
            "Epoch 31/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m269s\u001b[0m 304ms/step - accuracy: 0.9801 - loss: 0.0563 - precision: 0.9816 - recall: 0.9866 - val_accuracy: 0.9715 - val_loss: 0.0804 - val_precision: 0.9777 - val_recall: 0.9768\n",
            "Epoch 32/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m258s\u001b[0m 291ms/step - accuracy: 0.9821 - loss: 0.0511 - precision: 0.9827 - recall: 0.9890 - val_accuracy: 0.9737 - val_loss: 0.0794 - val_precision: 0.9720 - val_recall: 0.9824\n",
            "Epoch 33/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m304s\u001b[0m 340ms/step - accuracy: 0.9825 - loss: 0.0493 - precision: 0.9830 - recall: 0.9899 - val_accuracy: 0.9730 - val_loss: 0.0757 - val_precision: 0.9796 - val_recall: 0.9782\n",
            "Epoch 34/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m270s\u001b[0m 305ms/step - accuracy: 0.9835 - loss: 0.0490 - precision: 0.9837 - recall: 0.9897 - val_accuracy: 0.9737 - val_loss: 0.0743 - val_precision: 0.9800 - val_recall: 0.9785\n",
            "Epoch 35/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 300ms/step - accuracy: 0.9843 - loss: 0.0445 - precision: 0.9841 - recall: 0.9913 - val_accuracy: 0.9731 - val_loss: 0.0744 - val_precision: 0.9815 - val_recall: 0.9773\n",
            "Epoch 36/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m269s\u001b[0m 304ms/step - accuracy: 0.9863 - loss: 0.0407 - precision: 0.9865 - recall: 0.9915 - val_accuracy: 0.9748 - val_loss: 0.0715 - val_precision: 0.9814 - val_recall: 0.9795\n",
            "Epoch 37/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m355s\u001b[0m 341ms/step - accuracy: 0.9877 - loss: 0.0381 - precision: 0.9872 - recall: 0.9928 - val_accuracy: 0.9753 - val_loss: 0.0702 - val_precision: 0.9811 - val_recall: 0.9797\n",
            "Epoch 38/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 303ms/step - accuracy: 0.9875 - loss: 0.0377 - precision: 0.9873 - recall: 0.9929 - val_accuracy: 0.9754 - val_loss: 0.0686 - val_precision: 0.9810 - val_recall: 0.9804\n",
            "Epoch 39/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m321s\u001b[0m 302ms/step - accuracy: 0.9878 - loss: 0.0353 - precision: 0.9868 - recall: 0.9943 - val_accuracy: 0.9764 - val_loss: 0.0667 - val_precision: 0.9821 - val_recall: 0.9807\n",
            "Epoch 40/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m317s\u001b[0m 297ms/step - accuracy: 0.9884 - loss: 0.0348 - precision: 0.9881 - recall: 0.9937 - val_accuracy: 0.9770 - val_loss: 0.0663 - val_precision: 0.9823 - val_recall: 0.9808\n",
            "Epoch 41/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m373s\u001b[0m 354ms/step - accuracy: 0.9906 - loss: 0.0303 - precision: 0.9905 - recall: 0.9950 - val_accuracy: 0.9794 - val_loss: 0.0654 - val_precision: 0.9763 - val_recall: 0.9866\n",
            "Epoch 42/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m279s\u001b[0m 305ms/step - accuracy: 0.9896 - loss: 0.0311 - precision: 0.9884 - recall: 0.9950 - val_accuracy: 0.9788 - val_loss: 0.0656 - val_precision: 0.9755 - val_recall: 0.9876\n",
            "Epoch 43/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m305s\u001b[0m 345ms/step - accuracy: 0.9912 - loss: 0.0278 - precision: 0.9914 - recall: 0.9956 - val_accuracy: 0.9796 - val_loss: 0.0629 - val_precision: 0.9792 - val_recall: 0.9861\n",
            "Epoch 44/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m268s\u001b[0m 303ms/step - accuracy: 0.9915 - loss: 0.0276 - precision: 0.9909 - recall: 0.9956 - val_accuracy: 0.9796 - val_loss: 0.0612 - val_precision: 0.9824 - val_recall: 0.9850\n",
            "Epoch 45/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m365s\u001b[0m 352ms/step - accuracy: 0.9916 - loss: 0.0252 - precision: 0.9914 - recall: 0.9964 - val_accuracy: 0.9805 - val_loss: 0.0598 - val_precision: 0.9801 - val_recall: 0.9864\n",
            "Epoch 46/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m274s\u001b[0m 298ms/step - accuracy: 0.9926 - loss: 0.0233 - precision: 0.9922 - recall: 0.9963 - val_accuracy: 0.9804 - val_loss: 0.0596 - val_precision: 0.9819 - val_recall: 0.9857\n",
            "Epoch 47/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m272s\u001b[0m 308ms/step - accuracy: 0.9932 - loss: 0.0226 - precision: 0.9925 - recall: 0.9968 - val_accuracy: 0.9799 - val_loss: 0.0595 - val_precision: 0.9825 - val_recall: 0.9844\n",
            "Epoch 48/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m313s\u001b[0m 298ms/step - accuracy: 0.9935 - loss: 0.0211 - precision: 0.9926 - recall: 0.9975 - val_accuracy: 0.9802 - val_loss: 0.0591 - val_precision: 0.9816 - val_recall: 0.9868\n",
            "Epoch 49/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m331s\u001b[0m 307ms/step - accuracy: 0.9942 - loss: 0.0200 - precision: 0.9933 - recall: 0.9976 - val_accuracy: 0.9809 - val_loss: 0.0576 - val_precision: 0.9818 - val_recall: 0.9872\n",
            "Epoch 50/50\n",
            "\u001b[1m884/884\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m272s\u001b[0m 307ms/step - accuracy: 0.9939 - loss: 0.0205 - precision: 0.9928 - recall: 0.9972 - val_accuracy: 0.9821 - val_loss: 0.0576 - val_precision: 0.9811 - val_recall: 0.9876\n",
            "Restoring model weights from the end of the best epoch: 49.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ],
      "source": [
        "history = detector.fit(train_dataset,\n",
        "                    epochs=50,\n",
        "                    validation_data=val_dataset,\n",
        "                    initial_epoch=25,\n",
        "                    callbacks=[early_stopping])\n",
        "\n",
        "detector.save('/content/drive/MyDrive/DETECTOR_005/detector_005_1_50.keras')\n",
        "detector.save('/content/drive/MyDrive/DETECTOR_005/detector_005_1_50.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RFIB39KUDUIr"
      },
      "source": [
        "## RESULTS AFTER 50 EPOCHS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "AHYarNIk-fvB",
        "outputId": "c7ec67dc-b392-4d83-bb10-048789867d32"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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UrlyZ8ePHAzB8+HDat2/PhAkT6N69O7NmzWL9+vVMnz49d5tz5syhQoUKVK1ala1btzJ8+HBiYmLo0qWLIftY7Fk9IaCa64GPGalw5jBgcoUmERGRMsTQUNS3b19OnDjBiy++SFJSEk2aNGHx4sW5g6kPHz6c5xJTmzZtmDlzJs8//zyjR4+mdu3azJ8/n4YNG+a2OX78OLGxsSQnJxMaGkr//v154YUXinzfShSTCfyquKYKyTzpuo3/0lO0/yA8PJwRI0YwYsQIQ8oUEREpTIYPtB42bBjDhg3L92dLly69bF3v3r3p3bv3Fbf35JNP8uSTTxZUeWWHyQT+Ya5gdP4UnD4IVAdPf6MrExERKRKGT/MhxYjJBAFVfxtj5ITTB+DCjY3kFxERKSkUisqI6dOnU6lSpcseGHnnnXfy0EMPsW/fPu68806CQ0LwCWtIi+4D+GH5Gji1X8FIRETKBIWiguB0uqbUuNrFnnlt7a+0/MWzl/6sd+/enDx5kp9++il33alTp1i8eDH3338/586do1u3bsTHx7Np0ya6du9Jj0EjOHz0GJw64Hroo4iISClm+JiiUsGeCf+udFVNzUBAQX3u6GOuSWGvQrly5bj99tuZOXMmHTt2BOCrr74iKCiIDh06YDabiYyMzG0/7pVXmDd/Pt/8uJZhD8a4zhhdQwgTEREpaXSmqAy5//77+frrr3Pnefv888+59957MZvNnDt3jmeeeYaIiAgCAgLw8fFhx44dHE7NdM2X5nSAww4Xr39qExERkeJMZ4oKgtXLddbmKjgcDtLPnsXP1/fGn2ht9fr7Nn/Qo0cPnE4nCxcupEWLFqxYsYI333wTgGeeeYa4uDjeeOMNatWqhaenJ/fccw/ZdjuUq+46UwSQmQrZmeB+bZ8tIiJS3CkUFQST6aovY+FwgDXH1b6Ip/mw2WzcddddfP755+zdu5e6devStGlTAFatWsXAgQPp1asXAOfOnePgwYOuN5otEFjD9ewipwNO7oWgWtccykRERIozhaIy5v777+eOO+5g+/btPPDAA7nra9euzdy5c+nRowcmk4kXXngh751qZguY3cDiDs4cOLkPytdyPRFbRESkFNCYojLmtttuIzAwkF27dnHfffflrp84cSLlypWjTZs29OjRg+jo6NyzSHl4l3cFIcdF1xkj+4UirF5ERKTw6ExRGWM2mzl27PLxT+Hh4fz444951g0dOjTP69zLaTm/BaKL53+/lOZmK6ySRUREioTOFMm1s7i5Lp252Vx3pKXuhYtZRlclIiJyQxSK5PrkBiMPVzA6uVe364uISImmUCTXz2J1BSOLB+Rku4JRjoKRiIiUTApFcmMs7r8FI3fIyfotGNmNrkpEROSaKRRdJ6emvPid22/ByGx1jS1K2eGaLy0j9YpjjdR/IiJS3Ojus2tktVoByMzMxNNTz+jJ5ebhugvt5D7XJbQLZ1wLuM4iefi6FncfsFjJznZdZrNYLIaVLCIi8kcKRdfIYrEQEBBASkoKAF5eXphMpqt+v8PhIDs7mwsXLtz4NB/FkV91sJ8HewZkZ7ieY3QxC7KygFQAHGYPTqRl4+U4j5sjC7AaWrKIiAgoFF2XkJAQgNxgdC2cTifnz5/H09PzmsJUieV0d4Wii1lw8cJvA7GdmM+fouq6FzDNS4OwVlDjVtdS6SbXnW0iIiJFTL99roPJZCI0NJSKFStit1/boGK73c7y5ctp165d7qW4MiXjJBz9GfeUNZg9POFCKhxa6Vp+egU8/CC87e8hKaiOa245ERGRQqZQdAMsFss1j4mxWCxcvHgRm81WNkORrTKUrwyNY8DphFP7Yf9S13JguWsc0q5FrgWgbne45wPNsSYiIoVOoUiMYzJB+ZqupcVgcORA0pbfQ9LBVbBrIXx2N/SbBTY/oysWEZFSrBSO9JUSy2xxjSlq+xT0XwADvnFdTju0Cj6+w3WLv4iISCFRKJLiq1obGPAteAXB8V/gw9sh7ajRVYmISCmlUCTFW6Um8NBi8KsMqbthRlfXs5BEREQKmEKRFH9BtV3BKLAmpB12BaOkrUZXJSIipYxCkZQMAVVdwSi4EWSkwEfd4fBao6sSEZFSRKFISg6fijDwOwhrDRfS4NMY2Pej0VWJiEgpoVAkJYtnADw4F2p2BHsmfN4Hfl1gdFUiIlIKKBRJyePu7XpuUf0YcNhhzkDY9JnRVYmISAmnUCQlk5s73DMDbnoQnA5YMBQSphpdlYiIlGAKRVJymS3Q822IGuZ6/b/R8OO/XNOHiIiIXCOFIinZTCbo8grc9rzr9fLX4PvnwOEwti4RESlxFIqk5DOZoN1I6PaG6/W692D+Y5Bz0di6RESkRFEoktKj5RDoNR1MFtgyC+YMAPsFo6sSEZESQqFISpfIvtD3M7B4wM7vYGZvyDprdFUiIlICGB6Kpk6dSnh4ODabjVatWrFu3bq/bD9nzhzq1auHzWajUaNGLFq0KM/Pz507x7Bhw6hSpQqenp7Ur1+fadOmFeYuSHFTrxs88BW4+8CB5fDJnXD+tNFViYhIMWdoKJo9ezaxsbGMGTOGjRs3EhkZSXR0NCkpKfm2X716Nf369WPw4MFs2rSJmJgYYmJi2LZtW26b2NhYFi9ezGeffcaOHTsYMWIEw4YN45tvvimq3ZLioHo76P8NeJaDoxtw+7QHwWmb4PwZoysTEZFiytBQNHHiRIYMGcKgQYNyz+h4eXkxY8aMfNtPnjyZrl27MnLkSCIiIhg3bhxNmzZlypQpuW1Wr17NgAEDuPXWWwkPD+eRRx4hMjLyb89ASSlUpRkM+h58QjCd2Enr/W9inVgL3omC72Jhyxw4k2h0lSIiUky4GfXB2dnZbNiwgVGjRuWuM5vNdOrUiYSEhHzfk5CQQGxsbJ510dHRzJ8/P/d1mzZt+Oabb3jooYeoVKkSS5cuZffu3bz55ptXrCUrK4usrKzc1+np6QDY7Xbsdvv17N4VXdpeQW9XrqBcLRiwCNOKCVzYFY9PVhKk/Opa1n8AgNOvCs6wVjjDWuMIaw0V6oLJ8CvLJZ6OdWOo342hfjfGn/v9RvvfsFCUmppKTk4OwcHBedYHBwezc+fOfN+TlJSUb/ukpKTc12+//TaPPPIIVapUwc3NDbPZzPvvv0+7du2uWMv48eMZO3bsZeuXLFmCl5fXtezWVYuLiyuU7coVWKKhfjTu9nTKZ+wi8Nxuymfsxj/zEOb0I5i2H4HtX2MBsi3enPKuzUmfOpz0rkuaVzgOs9XoPSixdKwbQ/1uDPW7MS71e2Zm5g1tx7BQVFjefvtt1qxZwzfffEO1atVYvnw5Q4cOpVKlSnTq1Cnf94waNSrPGaj09HTCwsLo0qULfn5+BVqf3W4nLi6Ozp07Y7XqF21RudTv7bvdnaffc7LP4Ti6AVPiGtdydAPu9gxC0jcTkr4ZAKebDWelm3BWaY2zamuclVuArWCPi9JIx7ox1O/GUL8b48/9fulKz/UyLBQFBQVhsVhITk7Osz45OZmQkJB83xMSEvKX7c+fP8/o0aOZN28e3bt3B6Bx48Zs3ryZN95444qhyMPDAw8Pj8vWW63WQju4C3PbcmWX9bu1HNTp5FoAcuxwfAscTshdTJknMV16vRpw84Q7p0CjewzZh5JGx7ox1O/GUL8b41K/32jfGzZwwt3dnWbNmhEfH5+7zuFwEB8fT1RUVL7viYqKytMeXKfMLrW/NAbIbM67WxaLBYemfZCrYbG6Bmi3GQb3fg4j98Gw9dDjLYi8DwKqwsXz8PVgWKNHPYiIlCaGXj6LjY1lwIABNG/enJYtWzJp0iQyMjIYNGgQAP3796dy5cqMHz8egOHDh9O+fXsmTJhA9+7dmTVrFuvXr2f69OkA+Pn50b59e0aOHImnpyfVqlVj2bJlfPLJJ0ycONGw/ZQSzGSCoNqupdkA15xqi5+DddNdXzNOuOZdM5mMrlRERG6QoaGob9++nDhxghdffJGkpCSaNGnC4sWLcwdTHz58OM9ZnzZt2jBz5kyef/55Ro8eTe3atZk/fz4NGzbMbTNr1ixGjRrF/fffz6lTp6hWrRr/+te/ePTRR4t8/6QUMpvh9tfAuwL89C9Y8YYrGN3xJpgtRlcnIiI3wPCB1sOGDWPYsGH5/mzp0qWXrevduze9e/e+4vZCQkL48MMPC6o8kcuZTND+WfAOgoVPw8aPIfMk3P0BWG1GVyciItdJD2MRuV7NH4LeH4PF3TXP2md3w4U0o6sSEZHrpFAkciPq94QHvgZ3Xzi0Ej7sDmeT//59IiJS7CgUidyo6u1g0ELwrgjJW2FGFzi13+iqRETkGikUiRSE0EgY/D8oFw6nD8IH0XD8F6OrEhGRa6BQJFJQAmvAQ0sguBFkpLgupR1YYXRVIiJylRSKRAqSb7DrUlq1tpB9Fj67C379xuiqRETkKigUiRQ0m79r8HW9OyAnG+YMgPV6TISISHGnUCRSGKw26PMJNB0ATgd8NwKWvQZOp9GViYjIFSgUiRQWswV6TIZ2I12vf/oXfP+sa6oQEREpdhSKRAqTyeSaG+321wCTa860uQ/DxWyjKxMRkT9RKBIpCq3+AXf/F8xW2PY1zOwDWWeNrkpERP5AoUikqDS6B+7/EqzesP8n+LgHZKQaXZWIiPxGoUikKNW8DQZ+C17l4dgmeK8d/PRvOLHb6MpERMo8hSKRola5GTz0PwioCulHYdl/YGoLmHYLrJoMaUeMrlBEpExyM7oAkTIpqDY8vgZ2LoKtc2BfPCRtcS1xL0LVNq7LbfVjwLu80dWKiJQJCkUiRnH3hsa9XUvmKfh1Pmz9Gg6thMOrXcv3z7ouuTW8B+p1Aw9fo6sWESm1FIpEigOvQGj+kGtJOwrb57rOIB3/BfYscS1unlC3KzTqDbU6gZuH0VWLiJQqCkUixY1/ZWjzhGtJ3QNbv4JtX8HJvbB9nmux+UNED1dACr/F9aBIERG5IQpFIsVZUG3oMApu/Scc3/xbQJoLZ4/Bps9ci08wNLgLghuA4+LvS479Kl7bwZGT92fgGs/UoJehuy4iUtQUikRKApMJKt3kWjqPc4032joHfl0A55Jh7bsF+3k7v4Ndi6Hb62DzK9hti4gUUwpFIiWN2QzhbV3L7a/Dvh9d4SjzJJjdwOLm+mq25vPaAhbrX78+tR/WvANbZsHhBLjrfajayui9FhEpdApFIiWZm7tr8HXdrgW73YgeMHcInDkEH3aFds+6Jra16J8MESm99PBGEblc1dbw6Epo1AecDlj2Knx4O5w6YHRlIiKFRqFIRPJn84e733ddPvPwgyPrXE/d/mUWOJ1GVyciUuAUikTkrzXu4zprFNYass/CvH/A14Ph/BmjKxMRKVAKRSLy98pVg4ELocPzYLLAtq9hWls4uMroykRECoxCkYhcHYsbtB8Jg5dAueqQlggf3wHx41zPORIRKeEUikTk2lRpDo+ugCb3uwZhr3gDPugCJ/cZXZmIyA1RKBKRa+fhCzHvwD0fugZkH9voGoS98VMNwhaREkuhSESuX8O74LHVUK0t2DPgm2HwZX/IPGV0ZSIi10yhqIitPXBK/5GW0sW/Cgz4Bjq95Hoy9o5v4N2bMR1cYXRlIiLXRKGoCH28+iAPzFjP1wfMOBxKRlKKmC3Q9il4+AcoXwvOHsPy+V3UPzoLzp82ujoRkauiUFSEPNzMmEywItnMC9/8qmAkpU+lm+Afy6HZQEw4qZ2yCLc368HHPWDd+5B+zOgKRUSuSKGoCN3bsir/6dUQE06+3HCUZ776hRwFIylt3L2hx2Qu3vMJabYwTM4cOLAcFj0DEyPg/dtgxURI3Wt0pSIieWh2xyLW66ZKbN/6C5/tc2PuxqPYc5xM7BOJ1aJ8KqWLs243lu6DblERWPcshp3fQeI6OLrBtcSPhQr1oN4dEHEHhDYBk8noskWkDCsWv4mnTp1KeHg4NpuNVq1asW7dur9sP2fOHOrVq4fNZqNRo0YsWrQoz89NJlO+y+uvv16Yu3HVmgY5mdynMVaLiW9/OcYTMzeRfdFhdFkihaNcdbj5SddDH5/eCd0nQs3bXIOyT+x0Pedo+q0wqRF8/084uBIcOUZXLSJlkOGhaPbs2cTGxjJmzBg2btxIZGQk0dHRpKSk5Nt+9erV9OvXj8GDB7Np0yZiYmKIiYlh27ZtuW2OHz+eZ5kxYwYmk4m77767qHbrb0U3CGbaA81wt5hZvD2Jxz7bwAW7fhFIKecbAi0Gw4PzYOQ+12SzET3B6uV6Qvbad+Gj7vBGbVgwFHb/D+wXjK5aRMoIw0PRxIkTGTJkCIMGDaJ+/fpMmzYNLy8vZsyYkW/7yZMn07VrV0aOHElERATjxo2jadOmTJkyJbdNSEhInmXBggV06NCBGjVqFNVuXZWOEcG8P6A5Hm5m4nemMOST9QpGUnZ4Brgmm+37KTy7H+6dCZH3gWc5yDwJmz6DmX3g9ZowZyBsm6uAJCKFytBQlJ2dzYYNG+jUqVPuOrPZTKdOnUhISMj3PQkJCXnaA0RHR1+xfXJyMgsXLmTw4MEFV3gBal+nAh8ObIGn1cKKPakM+vBnMrMvGl2WSNGyekK97tDrXXhmL/T/Blo+Ar6VIPscbJ8HXw2CCXVg4TNw/BejKxaRUsjQgdapqank5OQQHBycZ31wcDA7d+7M9z1JSUn5tk9KSsq3/ccff4yvry933XXXFevIysoiKysr93V6ejoAdrsdu71gJ7q8tL0/brdFNX8+6N+UIZ9uJGH/Sfp/sJbpDzTF16Zx8AUlv36XwnVDfR7WxrV0egXTsc2Ydi/EvO1rTOlH4Of34ef3cQY3whF5P46G97jOOgmgY90o6ndj/Lnfb7T/S/1v3RkzZnD//fdjs9mu2Gb8+PGMHTv2svVLlizBy8urUOqKi4u7bN0jdWDaDgvrD52h1+R4Ho3IwavU/wkVrfz6XQpXwfR5M6hxExXO/krVk8sITduAJXkrliX/hLgXOO7fjMPl23HCtz6YDB8VUCzoWDeG+t0Yl/o9MzPzhrZj6K/coKAgLBYLycnJedYnJycTEhKS73tCQkKuuv2KFSvYtWsXs2fP/ss6Ro0aRWxsbO7r9PR0wsLC6NKlC35+fle7O1fFbrcTFxdH586dsVqtl/283dF0Bn28gUPn7Hx2tBwfDmhGOS/3Aq2hLPq7fpeCVzh9fgfwLI7MU7D9a8ybP8eSso0qZ9ZQ5cwanP5hOBr3wxF5n2v6kTJIx7ox1O/G+HO/X7rSc70MDUXu7u40a9aM+Ph4YmJiAHA4HMTHxzNs2LB83xMVFUV8fDwjRozIXRcXF0dUVNRlbT/44AOaNWtGZGTkX9bh4eGBh4fHZeutVmuhHdxX2vZN4eWZOaQ1D36wlu3HztL/ww189nArgnwur0+uXWH+mUr+CqXP/YOhzeMQ9ZhrfNGmT2HLHExpiVhWvIZlxetQ41Zo+qDrOUhuZe/vj451Y6jfjXGp32+07w0/zxwbG8v777/Pxx9/zI4dO3jsscfIyMhg0KBBAPTv359Ro0blth8+fDiLFy9mwoQJ7Ny5k5deeon169dfFqLS09OZM2cODz/8cJHuT0GoX8mPWY+0poKvBzuTztJv+hpS0nXXjchlTCao1AS6T4BndsFd/4Xq7QAn7P8JvnoIJtSF75+DpK1GVysixZzhoahv37688cYbvPjiizRp0oTNmzezePHi3MHUhw8f5vjx47nt27Rpw8yZM5k+fTqRkZF89dVXzJ8/n4YNG+bZ7qxZs3A6nfTr169I96eg1A72ZfYjrQnxs7En5Rx9p6/heNp5o8sSKb6sntC4Nwz4Fob/Au2eBb/Krglp106DaW3hvfauOdjOnzG6WhEphgwPRQDDhg3j0KFDZGVlsXbtWlq1apX7s6VLl/LRRx/lad+7d2927dpFVlYW27Zto1u3bpdt85FHHiEzMxN/f//CLr/Q1Kjgw5f/iKJygCcHUjPo814CiadubBCZSJlQLhxu+z8YsRUe+Brqx4DZCsc3u+Zge7MBxL0I5/J/SKyIlE3FIhTJlVUt78Xsf7SmaqAXiafOc+/0NRw6mWF0WSIlg9kCtTpBn4/h6V3Q9VWoWN/17KNVk3+bWuQ5SDtqdKUiUgwoFJUAVcp58eU/oqgR5M3RM+fp814C+06cM7oskZLFuzy0fgweWw33fQmVm8PFC65La5Mj4dvhcOqA0VWKiIEUikqIEH8bs/7RmtoVfUhOz6Lve2vYlXTW6LJESh6TCepEw8M/QP8FEH4LOOyw4SN4uxnMexRS9xhdpYgYQKGoBKnoa2PWI62JCPUj9VwW/d5fw9YjaUaXJVIymUyu2/YHfgeDFrsuszlz4JcvYEoLmDMIkrb97WZEpPRQKCphyvt48MWQVjSq7M+pjGzunZ7Ayj2pRpclUrJVi3INyB7yI9TtDjhh+1yYdjN8cR8c3WB0hSJSBBSKSqAAL3dmDmlFm5rlycjOYdBH61iwWQNFRW5Y5WbQbyY8ugoa3AWYYNdCeP82+PQuOJT/xNMiUjooFJVQvjYrHw5qQffGodhznAyftZn/rthvdFkipUNIQ+j9IQxdB5H9wGSBffHwYVf4sDvsXwpOp9FVikgBUygqwTzcLLx9700MbBMOwCsLdzB+0Q4cDv1jLVIgKtSBXtPgiQ3QbKDrWUeHVsInd8IHnWHXYoUjkVJEc7CXcGaziTE96lPRz4PXFu/iveX7OXE2i//c0xirRZlXpEAEVocek11PyV79lutOtSM/wxd9weIBPhXBu8IfvgbnXecT7Pre5u8a4C0ixZJCUSlgMpl4/NZaVPDx4J9ztzJ301FSM7J59/6meHvoj1ikwPhXhtv/A7c8DQlT4OcPXA+CTEt0LX/H4g7eFcGnwu9Byafib+sqQkhjKF9TwUnEIPqNWYr0bh5GkI8Hj3++keW7T9Dv/TXMGNiCIJ+yN0O4SKHyqQidX4YO/wdnkyDjhGvKkIwU19fc70/8vi4rHXKyIf2Ia7kSz0AIa/nb0goqNQV3r6LbN5EyTKGolOlQryIzh7TioY9+ZsuRNO55dzWfPNSKquX1j6pIgXPzgHLVXMvfsV/4U1BKzhua0o/C8S1w/hTsXuxaAMxuENzQFZAuBSX/KjqbJFIIFIpKoZuqluOrx9rQ/4N1HDyZyV3vruajQS1oWLnkTo4rUuJZbRBQ1bVcycVsSNoCiesgca1rOXvcNZHt8c2w7j1XO9/Q3wNSWCvXZTc396LYC5FSTaGolKpZwYe5j7dhwIx17Ew6y73T1zD9wWa0qRVkdGkiciVu7lCluWuJetx1Z1vaETiy7vegdHyLKyj9usC1gGuwd6WbIKwlptBmeGalwumD4MwG+3mwZ0J2puurPfPa1nkHQceXoEozI3tGpEgoFJViwX42vnw0ikc+Wc+a/acY8OE6JvZpQo/ISkaXJiJXw2SCgDDX0vBu17rsTDi26bczSb8FpfOnIHENJK7BDegC8GsB1vFBJ2j9OHQYDe7eBbhhkeJFoaiU87NZ+WhQS2K/3MyirUk8OWsTqeeyGHRzdaNLE5Hr4e4F4Te7FnCdTTq1P/dymzNxHc6UXZisNkzuXmD1BKv3b1+9XO/Ps87TFXTyW+dmg80zYeuXrrvtdnwLPd9yzRknUgopFJUBNquFt/s1JchnO58kHGLst7+ScjaLZ6PrYtJgTZGSzWRy3cZfviY0uY+LdjuLFi2iW7duWK3WG99+zQ7QqDd89xScOeR6cOVND0CXV8Cz3I1vX6QY0dP9ygiL2cTYng0YGV0XgHeX7uOZOVuw5zgMrkxEir06XWDoGmgxxPV602cwtRX8+o2xdYkUMIWiMsRkMjG0Qy1eu7sxFrOJrzce4ZFP1pOZfdHo0kSkuPPwhe5vwKDFUL6265ECXz4Isx+Es8lGVydSIBSKyqA+LcKY/mAzbFYzP+06wX3vr+VURrbRZYlISVAtCh5d6Xqqt8kCO76BqS1cZ480D5yUcApFZVTHiGA+f7g1AV5WNiee4Z53V7PvxDmjyxKRksBqg44vwiNLITQSLqTBgqHwaQycOmB0dSLXTaGoDGtWrRxfPRpF5QBP9qdmEDNlFXG/6jS4iFyl0Mbw8I+uKU/cbLB/KbzbBhKmgiPH6OpErplCURlXq6Iv84feTMvwQM5mXWTIJ+uZGLcbh0OnwUXkKljc4Obh8NhqCL/F9dDH/42GD7pAckE+LEmk8CkUCRV8Pfh8SCsGtgkH4K34PTz8yXrSztuNLUxESo7yNaH/N9BjMnj4wdH18F47+Gk8XMwyujqRq6JQJABYLWZe6tmAiX0i8XAz8+POFO6cspLdyWeNLk1ESgqzGZoNhKFroW43cNhh2auucJT4s9HVifwthSLJ466mVfj6sTZUDvDk4MlMYqauYuGW40aXJSIliV8luHcm3PMheFeAEzvhg86uB0Ce2m90dSJXpFAkl2lY2Z9vn2jLzbXKk5mdw9CZGxn//Q5yNM5IRK6WyQQN74Kh6yCyH+CE9TPgrabwxX1wcKVu4ZdiR6FI8hXo7c7Hg1rySLsaALy3bD8DP1zHaT3PSESuhVcg9JoGA76DWp0BJ+xaCB91d11W+2UWXNS/K1I8KBTJFblZzIzuFsHb/W7C02phxZ5UekxZyfZjaUaXJiIlTfVb4IGvXGeOmg0CN09I2gLz/gGTGsHy1yHjpNFVShmnUCR/q0dkJeYNbUPVQC+OnD7P3e+uZv6mo0aXJSIlUYW60GMSxP4Kt70APiFwLgl+fAXerA/fDocTu4yuUsoohSK5KvVC/Ph2WFturVuBC3YHI2ZvZuy32zWhrIhcH69AaPcMjNgKvaa7nox98QJs+AimtoTP7oa98Rp3JEVKoUiumr+XlQ8GtGBYh1oAfLjqIA/8dy2p5/QMEhG5Tm7uENkXHlkGAxdBvTsAE+z9AT67C96Jgg0fg/280ZVKGaBQJNfEYjbxTHRd3nuwGT4ebqw9cIoeb69kc+IZo0sTkZLMZILwm+Hez+HJjdDqUXD3gRM74Nsn4c0G8OO/4KymIpLCo1Ak1yW6QQjzh7ahRgVvjqddoM+0BGb/fNjoskSkNAisAbf/xzXuqMsr4B8GmSdh+WswqSHMewwOrwWHLt9LwVIokutWq6IvC4beTOf6wWTnOHju663837ytZF/UP1QiUgBs/tDmCXhyM/T+CKq0hJxs+GUmzOjiOnu06Fk4uEoT0EqBUCiSG+Jrs/LeA814unMdTCb4fO1h+r2/hpSzF4wuTURKC4sbNOgFD8fBw/HQuC+4+8LZY7DuPfioG0yoB9/Fwv5lkHPR6IqlhDI8FE2dOpXw8HBsNhutWrVi3bp1f9l+zpw51KtXD5vNRqNGjVi0aNFlbXbs2EHPnj3x9/fH29ubFi1acPiwLu0UFrPZxBMdazNjQAt8bW5sOHSaO6esYsuRM0aXJiKlTZXmcNd0GLkX+s2GyPtcZ5QyUmD9B/BJT5hQB7550jVYO0cTW8vVMzQUzZ49m9jYWMaMGcPGjRuJjIwkOjqalJSUfNuvXr2afv36MXjwYDZt2kRMTAwxMTFs27Ytt82+ffto27Yt9erVY+nSpWzZsoUXXngBm81WVLtVZnWoV5EFQ2+m5m/jjHpPS2DBZj3PSEQKgdUGdbtCr3fhmb1w/9dw04PgGegaf7TxY9dt/a/XgvmPw+7/wUXdKSt/zdBQNHHiRIYMGcKgQYOoX78+06ZNw8vLixkzZuTbfvLkyXTt2pWRI0cSERHBuHHjaNq0KVOmTMlt83//939069aN1157jZtuuomaNWvSs2dPKlasWFS7VabVqODDvKE3c1u9imRddDB81mZe/X6n5k0TkcLj5g61O8GdU+CZPdB/ATR/yDUZ7YUzsPlzmNnHFZDmPgI7vtMt/pIvN6M+ODs7mw0bNjBq1KjcdWazmU6dOpGQkJDvexISEoiNjc2zLjo6mvnz5wPgcDhYuHAhzz77LNHR0WzatInq1aszatQoYmJirlhLVlYWWVm//w8iPT0dALvdjt1esKdeL22voLdbnHha4J1+kbz5w17eW3GAacv2seN4Gm/2boSvzWpITWWh34sb9bkx1O9A2M2upfN4TIlrMO38FvPO7zCdS4Its2HLbJxWb5y1O+Oo2x38q+L08HU9AsDDF9y9wXRt5wzU78b4c7/faP8bFopSU1PJyckhODg4z/rg4GB27tyZ73uSkpLybZ+UlARASkoK586d49VXX+WVV17hP//5D4sXL+auu+7ip59+on379vlud/z48YwdO/ay9UuWLMHLy+t6du9vxcXFFcp2i5P6QP/aJr7Ya2bZ7lRun/gjD9fNoaKncTWVhX4vbtTnxlC//1E7qNWWchn7qHRmHZXO/IyX/RSmX+dj/nV+vu+4aPbgotmTixYbF8027JZL3+f/1W62UcFiY+O8neSYrDjMVnJM7uSYrTguvTa74zRZinbXy4hLx3tmZuYNbcewUFQYHL89s+LOO+/kqaeeAqBJkyasXr2aadOmXTEUjRo1Ks8ZqPT0dMLCwujSpQt+fn4FWqPdbicuLo7OnTtjtRpz1qQodQN6HU3nsZmbSErP4q2dNib3acwttYOKtI6y1u/FgfrcGOr3q+B0cvHYJky7vsW8f6nrElvWWcg+h8nhunPNzZGFmyMLCvhGNqfJAm4215goiwe4eYCbDaebLfd73Gzg4YPTJxi8K7q++gTj9AkBn2DX2SyTqWALK6H+fLxfutJzvQwLRUFBQVgsFpKT8z6dNDk5mZCQkHzfExIS8pftg4KCcHNzo379+nnaREREsHLlyivW4uHhgYeHx2XrrVZrof2jUpjbLm5uCi/PN0+05bHPNrLh0Gke/nQjo26P4OFbqmMq4r/YZanfiwv1uTHU738jvJVr+SOn0zUYO+ssZJ+FrHO5YYmss3/6/txvbVzfO7LOcjb1GH5e7pguZrvmcbuYBRfPu56t9BuTMwfsGa7lD67pX0I3T/CpCL6/hSSfYPANdk2u6xvi+plPCHgHgblsnJm6dLzf6DFvWChyd3enWbNmxMfH5473cTgcxMfHM2zYsHzfExUVRXx8PCNGjMhdFxcXR1RUVO42W7Rowa5deWdY3r17N9WqVSuU/ZCrU9HXxswhrXhx/nZmr0/kX4t2sON4Ov++qxE2a9n4SysixZzJ5DqDY7UBFa7prTl2O0sXLaJbt26X/2J2OCAnyzW4+2JW3sD0x9d//PmFNDiXDGeTXF/PJbumOMlKc73vzCHX8pf7YwbvilDpJmjSD+rc7hqULldk6OWz2NhYBgwYQPPmzWnZsiWTJk0iIyODQYMGAdC/f38qV67M+PHjARg+fDjt27dnwoQJdO/enVmzZrF+/XqmT5+eu82RI0fSt29f2rVrR4cOHVi8eDHffvstS5cuNWIX5Q883Cy8encjIkJ9GbdwB3M3HWVfagbTH2xGsJ8emSAipZTZDGZPsBbAgMrszN9D0qWgdC7pT1+TIeMEOB2udbu/dy2egdC4DzS5H0Ib33gtpZChoahv376cOHGCF198kaSkJJo0acLixYtzB1MfPnwYs/n3OwDatGnDzJkzef755xk9ejS1a9dm/vz5NGzYMLdNr169mDZtGuPHj+fJJ5+kbt26fP3117Rt27bI908uZzKZGHhzdWoH+zJ05kZ+STxDj7dX8t6DzbipajmjyxMRKd7cvSCwumv5KzkXXcEo/Sjs+BZ+meUKSGunuZaQRtDkAWjUG7zLF03tJYDJ6XTqATJ/kp6ejr+/P2lpaYUy0HrRlU6xljGHTmYw5JP17E4+h7ubmfG9GnF3syqF8lnq96KnPjeG+t0Yxb7fcy7Cvh9dz2zatej3cU5mq+shmE0egFqdXFOqlCB/7vcb/f19XQ9v/Pjjj1m4cGHu62effZaAgADatGnDoUN/c41T5DfVynsz9/HfJpS96ODpOb/wyne/cjFHE8qKiBQoixvU6QJ9Poand8Htr0NoJDjsrjNJX/SFN+vDkhfgxK6/314pdV2h6N///jeenq5rowkJCUydOpXXXnuNoKCg3FvhRa6Gj4cb7z3QjCdvqwXAf1ceYNBHP5OWqQegiYgUCq9AaPUI/GM5PLoKWj8OXuVdY5FWvwVTW8L7HWH9DNeA7zLkukJRYmIitWq5fonNnz+fu+++m0ceeYTx48ezYsWKAi1QSj+z2URsl7pMva8pnlYLK/akEvPOKvamnDW6NBGR0i2kIXQdD7E7oe9nrjvUTBY4uh6+ewreqANfPwz7fnLdRXejnE7XpTz7BbiY/ffti9h1XTz08fHh5MmTVK1alSVLluQ++NBms3H+vOaTkevTvXEo4UFePPLJBg6kZhAzdTWv39OY2xuFGl2aiEjp5uYOET1cy9lk13Qomz+HEzth6xzX4lcFylUDx8U/LDl/+j7nL35+EZw5v3/mLc9AxxeM2+d8XFco6ty5Mw8//DA33XQTu3fvplu3bgBs376d8PDwgqxPypgGlfz5ZtjNPPb5RtYdOMVjn2/k9oYhjO3ZgIq6bV9EpPD5BsPNT0KbJ+DYRtj0OWz7CtKPuJaC4ijgx4UXgOsKRVOnTuX5558nMTGRr7/+mvLlXbfzbdiwgX79+hVogVL2lPfx4LPBrZgcv5v3lu3n+21JrNqbyv91j6BP87Aifwq2iEiZZDJB5WauJfrfcGA52DNdT8k2u/22/PH7316b8ll32evfpjspZq4rFAUEBDBlypTL1uc3qarI9XB3MzMyuh7dG1Xin3O3sOVIGs99vZX5m44x/q5GhAd5G12iiEjZYbW57l4r5a5roPXixYvzzCU2depUmjRpwn333cfp06cLrDiR+pX8mPtYG57vHoHNaiZh/0miJy1n2rJ9unVfREQK1HWFopEjR+bORLt161aefvppunXrxoEDB/LMNi9SENwsZh6+pQZLRrSnba0gsi46ePX7ndw5dRXbjpat20VFRKTwXFcoOnDgQO5M9F9//TV33HEH//73v5k6dSrff/99gRYocknV8l58Orglb/SOxN/TyvZj6dw5dRXjv9/B+eycv9+AiIjIX7iuUOTu7k5mZiYAP/zwA126uK4zBgYG5p5BEikMJpOJe5pV4YfY9tzROJQch5P3lu2n6+TlrN6banR5IiJSgl1XKGrbti2xsbGMGzeOdevW0b17dwB2795NlSqFM3eVyB9V8PVgyn1N+W//5oT42Th0MpP7/ruW577aoqdhi4jIdbmuUDRlyhTc3Nz46quvePfdd6lcuTIA33//PV27di3QAkX+Sqf6wcTFtuPB1tUAmL0+kU5vLuP7rcfRXMciInItruuW/KpVq/Ldd99dtv7NN9+84YJErpWvzcq4mIb0bFKJf369hX0nMnjs8410qR/My3c2JMS/+D0LQ0REip/rCkUAOTk5zJ8/nx07dgDQoEEDevbsicViKbDiRK5Fi/BAFj55C+/8tJd3lu5jya/JJOw7yahuEdzdJMTo8kREpJi7rlC0d+9eunXrxtGjR6lbty4A48ePJywsjIULF1KzZs0CLVLkatmsFmK71KVb41Ce+3orvySeYfS8rczbdIToAKOrExGR4uy6xhQ9+eST1KxZk8TERDZu3MjGjRs5fPgw1atX58knnyzoGkWuWb0Q10MfX7yjPp5WCz8fPM1rWyzMXn9EY41ERCRf1xWKli1bxmuvvUZgYGDuuvLly/Pqq6+ybNmyAitO5EZYzCYealudJU+1o1X1cmQ7TDy/4FeGfLKB1HNZRpcnIiLFzHWFIg8PD86ePXvZ+nPnzuHu7n7DRYkUpLBALz4Z2Jw7q+VgtZj4YUcyXSctJ35HstGliYhIMXJdoeiOO+7gkUceYe3atTidTpxOJ2vWrOHRRx+lZ8+eBV2jyA0zm03cVsnJ3EdbUzfYl9Rz2Qz+eD2j5m4lI+ui0eWJiEgxcF2h6K233qJmzZpERUVhs9mw2Wy0adOGWrVqMWnSpAIuUaTg1AvxZcGwm3m4bXUAvlh3mO5vrWDTYU1kLCJS1l3X3WcBAQEsWLCAvXv35t6SHxERQa1atQq0OJHCYLNaeP6O+txWryLPzPmFgyczuWdaAsM61GLYbbWwWq7r/woiIlLCXXUoio2N/cuf//TTT7nfT5w48forEikibWoF8f2Idry4YBsLNh9jcvwelu4+waS+Tage5G10eSIiUsSuOhRt2rTpqtqZTKbrLkakqPl7Wpl87010jAjm+Xmu5xp1m7yC5++I4L6WVXU8i4iUIVcdiv54JkiktOkZWYnm1crxzJxfWL3vJP83bxs/7kjh1bsbU8HXw+jyRESkCGjwhMhvKgV48tngVjzfPQJ3NzPxO1PoOmk5cb/q1n0RkbJAoUjkD8xmEw/fUoNvh7WlXogvJzOyGfLJep77aotu3RcRKeUUikTyUfe3W/f/0a4GJhPMXp9It7dWsOGQbt0XESmtFIpErsDDzcKobhF8MaQ1lQM8OXQyk97TVjNhyS6yLzqMLk9ERAqYQpHI32hdozzfj7iFu26qjMMJb/+4lzveXsFGPfBRRKRUUSgSuQp+NisT+zZh6n1NKe/tzu7kc9z97mpe+mY75zTWSESkVFAoErkG3RuH8kNse+5pVgWnEz5afZAuE5dpclkRkVJAoUjkGpXzdueN3pF8NrgVVQO9OJZ2gcEfr2fYzI2cOJtldHkiInKdFIpErlPb2kH8b0Q7/tGuBhazie+2HKfTxGV8uT4Rp9NpdHkiInKNFIpEboCnu+sOtQVDb6ZBJT/Sztt59qstPPDBWg6dzDC6PBERuQbFIhRNnTqV8PBwbDYbrVq1Yt26dX/Zfs6cOdSrVw+bzUajRo1YtGhRnp8PHDgQk8mUZ+natWth7oKUcQ0r+7Ng6M2M7lYPm9XMqr0n6fLmcqYt28fFHN2+LyJSEhgeimbPnk1sbCxjxoxh48aNREZGEh0dTUpKSr7tV69eTb9+/Rg8eDCbNm0iJiaGmJgYtm3blqdd165dOX78eO7yxRdfFMXuSBnmZjHzSLuaLBnRnra1gsi66ODV73fSc8oqth5JM7o8ERH5G4aHookTJzJkyBAGDRpE/fr1mTZtGl5eXsyYMSPf9pMnT6Zr166MHDmSiIgIxo0bR9OmTZkyZUqedh4eHoSEhOQu5cqVK4rdEaFqeS8+HdySN3pHEuBl5dfj6dw5dSX/Wvgrmdm6fV9EpLhyM/LDs7Oz2bBhA6NGjcpdZzab6dSpEwkJCfm+JyEhgdjY2DzroqOjmT9/fp51S5cupWLFipQrV47bbruNV155hfLly+e7zaysLLKyfr9rKD09HQC73Y7dbr+eXbuiS9sr6O3KXzOi3+9sHEzbGgG8smgX321N4v0VB/h+WxIv94zgllpBRVaHUXSsG0P9bgz1uzH+3O832v+GhqLU1FRycnIIDg7Osz44OJidO3fm+56kpKR82yclJeW+7tq1K3fddRfVq1dn3759jB49mttvv52EhAQsFstl2xw/fjxjx469bP2SJUvw8vK6nl37W3FxcYWyXflrRvR7Zx+oVM/EnP1mjpw+z0Mfb6RFkIOYcAc+1iIvp8jpWDeG+t0Y6ndjXOr3zMzMG9qOoaGosNx777253zdq1IjGjRtTs2ZNli5dSseOHS9rP2rUqDxnn9LT0wkLC6NLly74+fkVaG12u524uDg6d+6M1VoGfiMWE0b3ezfg8ayLvBm/l0/WHObnVDN7Mz14vls9ejQOwWQyFXlNhc3oPi+r1O/GUL8b48/9fulKz/UyNBQFBQVhsVhITs77NODk5GRCQkLyfU9ISMg1tQeoUaMGQUFB7N27N99Q5OHhgYeHx2XrrVZroR3chbltuTIj+z3AamXsnY2IuakKo+ZuZWfSWZ7+aivxu07wr5hGlPN2N6SuwqZj3Rjqd2Oo341xqd9vtO8NHWjt7u5Os2bNiI+Pz13ncDiIj48nKioq3/dERUXlaQ+u02ZXag9w5MgRTp48SWhoaMEULnIDbqpajm+faMvTnevgZjaxaGsS0ZOWs2z3CaNLExEp0wy/+yw2Npb333+fjz/+mB07dvDYY4+RkZHBoEGDAOjfv3+egdjDhw9n8eLFTJgwgZ07d/LSSy+xfv16hg0bBsC5c+cYOXIka9as4eDBg8THx3PnnXdSq1YtoqOjDdlHkT+zWsw80bE284feTK2KPqSczWLAjHWMWbCN89k5RpcnIlImGR6K+vbtyxtvvMGLL75IkyZN2Lx5M4sXL84dTH348GGOHz+e275NmzbMnDmT6dOnExkZyVdffcX8+fNp2LAhABaLhS1bttCzZ0/q1KnD4MGDadasGStWrMj3EpmIkRpW9ue7J9oysE04AB8nHOKOt1fouUYiIgYoFgOthw0blnum58+WLl162brevXvTu3fvfNt7enryv//9ryDLEylUNquFl3o24LZ6FRn51S/sO5FBr3dWMbxjbR67tSZuFsP/7yIiUiboX1uRYqJdnQr8b0Q7ujcK5aLDyYS43fR5L0FzqImIFBGFIpFiJMDLnSn33cSbfSPx9XBj4+Ez3D55BV+sO4zT6TS6PBGRUk2hSKSYMZlM9LqpCoufakfrGoFkZucwau5WhnyygdRzWX+/ARERuS4KRSLFVOUAT2Y+3JrR3erhbjHzw45kot9czg+/Jv/9m0VE5JopFIkUY2aziUfa1WTBsJupF+LLyYxsHv5kPaPmbiEjS5PLiogUJIUikRIgItSP+UNv5pF2NTCZ4It1iXR7awUbDp02ujQRkVJDoUikhLBZLYzuFsHMh1tTyd/GoZOZ9J62mglLdmHPcRhdnohIiadQJFLCRNUsz/cj2tHrpso4nPD2j3u5653V7E05a3RpIiIlmkKRSAnk72nlzb5NmHLfTfh7Wtl6NI3bJ6/gle9+Je283ejyRERKJIUikRLsjsaV+N+IdtxWryL2HCf/XXmA295YyudrD5Hj0HONRESuhUKRSAkX4m9jxsAWfDioBTUreHMyI5v/m7eNO95eScK+k0aXJyJSYigUiZQSHepWZPGIdrx4R338bG7sOJ5Ov/fX8OinGzh8MtPo8kREij2FIpFSxGox81Db6iwd2YEHW1fDbILF25PoNHEZ/1m8k3N6tpGIyBUpFImUQoHe7oyLacj3w9vRtlYQ2TkO3l26jw5vLGXO+kQcGm8kInIZhSKRUqxuiC+fDm7J+/2bE17eixNnsxj51RZi3lnF+oOnjC5PRKRYUSgSKeVMJhOd6wfzv6faMbpbPXw93NhyJI17piXwxBebOHrmvNEliogUCwpFImWEh5uFR9rV5MdnbuXeFmGYTPDtL8foOGEpE+N2k5mt8UYiUrYpFImUMRV8PXj17sZ8O6wtLasHcsHu4K34PXScsIwFm4/idGq8kYiUTQpFImVUw8r+zH6kNe/c35Qq5Tw5nnaB4bM2c/e7q9l2NM3o8kREipxCkUgZZjKZ6NYolB9i2zMyui5e7hY2Hj5Dzykr+dfCX3VJTUTKFIUiEcFmtTC0Qy1+euZWekZWwuGE91ccoMuby1m++4TR5YmIFAmFIhHJFexn461+N/HhwBZUDvDkyOnz9J+xjqdmb+bkuSyjyxMRKVQKRSJymQ71KrLkqXYMujkckwnmbTpKp4nLmLvxiAZii0ippVAkIvny9nBjTI8GzHv8ZuqF+HI6007sl7/Qf8Y6zaUmIqWSQpGI/KUmYQF8+0RbRkbXxd3NzIo9qXSZtIzpy/dxMcdhdHkiIgVGoUhE/pbVYmZoh1r8b0Q7omqU54Ldwb8X7STmnVW6fV9ESg2FIhG5atWDvJk5pBWv3d0Yf08r246mc+fUVfx70Q7OZ+cYXZ6IyA1RKBKRa2IymejTIowfYttzR+NQchxOpi/fT5dJy1ixR7fvi0jJpVAkItelgq8HU+5ryoyBzankbyPx1Hke/GAdsbM3cyoj2+jyRESumUKRiNyQ2+oFsyS2PQPbuG7fn/vb7fsLNh9Dd++LSEmiUCQiN8zHw42XejZg7mNtqBvsy6mMbJ75ehvTdpg5fEq374tIyaBQJCIF5qaq5fj2ibY806UO7m5mdqaZ6fb2at6K30PWRQ3EFpHiTaFIRAqUu5uZYbfV5ruhUdT2c5B10cHEuN10nbSClXtSjS5PROSKFIpEpFBUD/JmaH0HE3s3ooKvBwdSM3jgg7UMm7mR5PQLRpcnInIZhSIRKTQmE/RoHEr8066B2GYTfLflOB0nLGPGygN6IraIFCvFIhRNnTqV8PBwbDYbrVq1Yt26dX/Zfs6cOdSrVw+bzUajRo1YtGjRFds++uijmEwmJk2aVMBVi8jV8rNZealnA74Z1pbIsADOZV3k5e9+peeUVWw8fNro8kREgGIQimbPnk1sbCxjxoxh48aNREZGEh0dTUpKSr7tV69eTb9+/Rg8eDCbNm0iJiaGmJgYtm3bdlnbefPmsWbNGipVqlTYuyEiV6FhZX/mPdaGf/dqhL+nlV+Pp3PXO6sZNXcLp/VsIxExmOGhaOLEiQwZMoRBgwZRv359pk2bhpeXFzNmzMi3/eTJk+natSsjR44kIiKCcePG0bRpU6ZMmZKn3dGjR3niiSf4/PPPsVqtRbErInIVzGYT97Wqyo9Pt+eeZlUA+GJdIrdNWMqXPyficOjhRiJiDENDUXZ2Nhs2bKBTp06568xmM506dSIhISHf9yQkJORpDxAdHZ2nvcPh4MEHH2TkyJE0aNCgcIoXkRtS3seDN3pH8uU/oqgb7MvpTDvPfr2FPu8lsON4utHliUgZ5Gbkh6emppKTk0NwcHCe9cHBwezcuTPf9yQlJeXbPikpKff1f/7zH9zc3HjyySevqo6srCyysrJyX6enu/5Bttvt2O32q9rG1bq0vYLervw19XvRu9o+v6mKL/Mea8Unaw7z1o/7WH/oNHe8vZIBravyxG018fEw9J+pEkfHujHU78b4c7/faP+Xun9tNmzYwOTJk9m4cSMmk+mq3jN+/HjGjh172folS5bg5eVV0CUCEBcXVyjblb+mfi96V9vnocCzDWHeQTO/nDIzY/Uhvl5/kLvCHUQGOrnKv87yGx3rxlC/G+NSv2dm3tgT9A0NRUFBQVgsFpKTk/OsT05OJiQkJN/3hISE/GX7FStWkJKSQtWqVXN/npOTw9NPP82kSZM4ePDgZdscNWoUsbGxua/T09MJCwujS5cu+Pn5Xe/u5ctutxMXF0fnzp011qkIqd+L3vX2+f3Ast0nGPvdThJPn+fD3RZuqVWeMXdEUK184fwnpTTRsW4M9bsx/tzvl670XC9DQ5G7uzvNmjUjPj6emJgYwDUeKD4+nmHDhuX7nqioKOLj4xkxYkTuuri4OKKiogB48MEH8x1z9OCDDzJo0KB8t+nh4YGHh8dl661Wa6Ed3IW5bbky9XvRu54+79SgEm3rBPPO0n1MW7qPFXtP0mXySpqHB9IpoiKdIoKpUcGnkCouHXSsG0P9boxL/X6jfW/45bPY2FgGDBhA8+bNadmyJZMmTSIjIyM3wPTv35/KlSszfvx4AIYPH0779u2ZMGEC3bt3Z9asWaxfv57p06cDUL58ecqXL5/nM6xWKyEhIdStW7dod05ErpvNaiG2cx163VSZMd9sZ/nuE6w7cIp1B07x70U7qVHBm04RwXSKCKZp1QDcLIbfTCsiJZzhoahv376cOHGCF198kaSkJJo0acLixYtzB1MfPnwYs/n3f+zatGnDzJkzef755xk9ejS1a9dm/vz5NGzY0KhdEJFCVD3Im08easmR05nE70jhhx3JrNl/kv0nMph+Yj/Tl+8nwMvKbXUr0jEimHZ1gvC16X/qInLtDA9FAMOGDbvi5bKlS5detq5379707t37qref3zgiESlZqpTzYkCbcAa0CefsBTvLd6cSvyOZH3elcCbTztxNR5m76ShWi4nWNcrTKSKYjhEVqVJO45BE5OoUi1AkInItfG1WujcOpXvjUC7mONh4+Aw/7Ejmhx3J7D+RwYo9qazYk8qYb7ZTL8SXzvWD6RgRTOPK/pjNuo1NRPKnUCQiJZqbxUzL6oG0rB7I6G4R7D9xjvgdKcTtSGb9wVPsTDrLzqSzvP3jXir4etCxXkV6NqlEm5pBRpcuIsWMQpGIlCo1KvhQo4IPQ9rV4HRGNkt3p/DDryks232CE2ezmPVzIrN+TqRrgxDG9KxPqL+n0SWLSDGhUCQipVY5b3d63VSFXjdVIfuig7UHTrJoaxJfrk9k8fYklu85QWznOgxsE66710TE+AlhRUSKgrubmVtqV2D8XY1Y+GRbmlcrR2Z2Dq8s3MEdb69kw6FTRpcoIgZTKBKRMqdeiB9f/iOK/9zdiAAvKzuTznL3uwn88+stnM7INro8ETGIQpGIlElms4m+Lary49O30qd5FQBm/ZxIx4nLmLM+EafTaXCFIlLUFIpEpEwL9HbntXsimfNoFHWCfTiVkc3Ir7bQ97017E4+a3R5IlKEFIpERIAW4YEsfPIWRt1eD0+rhXUHT9Ft8gpe/X4nmdkXjS5PRIqAQpGIyG+sFjP/aF+TH55uT+f6wVx0OJm2bB+dJy4n7tdko8sTkUKmUCQi8ieVAzx5v39z/tu/OZUDPDl65jxDPlnPkE/Wc/TMeaPLE5FColAkInIFneoHExfbjkfb18TNbCLu12Q6TVjGtGX7sOc4jC5PRAqYQpGIyF/wcnfjn7fXY9HwW2gZHsh5ew6vfr+T7m+tYN0BPdtIpDRRKBIRuQp1gn2Z/Y/WvH5PYwK93dmdfI4+7yXw1OzNHD6ZaXR5IlIAFIpERK6SyWSid/Mw4mPb069lGADzNh2l48SlvDB/GynpFwyuUERuhEKRiMg1Kuftzvi7GvPNsJu5pXYQ9hwnn645RLvXf+LV73dyJlNPxRYpiRSKRESuU+MqAXw6uBVfDGlN06oBXLA7mLZsH7e89hNTftxDRpaebyRSkigUiYjcoKia5fn6sTZ8MKA59UJ8OXvhIm8s2U27135ixsoDXLDnGF2iiFwFhSIRkQJgMpnoGBHMoidvYfK9TQgv78XJjGxe/u5XbntjKV/+nMhF3cYvUqwpFImIFCCz2cSdTSoTF9ue8Xc1IsTPxrG0Czz79Ra6TFrOwi3HcTg02axIcaRQJCJSCKwWM/1aVmXpyFt5vnsE5bys7D+RwdCZG+kxZSU/7UrB6VQ4EilOFIpERAqRzWrh4VtqsPzZDozoVBsfDze2H0tn0Ic/0/e9Nfx8UA+AFCkuFIpERIqAr83KiE51WP5sBx5pVwMPNzPrDp6i97QEBn64jm1H04wuUaTMUygSESlCgd7ujO4WwbKRHbivVVXczCaW7jrBHW+v5B+frmfrEYUjEaMoFImIGCDE38a/ezXih9j2xDSphMkE/9ueTI8pK+k/Y53mVRMxgEKRiIiBwoO8mXTvTcQ91Y67bqqMxWxi+e4T9HkvgT7TEli2+4QGZIsUEYUiEZFioFZFXyb2bcJPT9/Kfa2q4m5xjTkaMGMdd05dxf+2J+lWfpFCplAkIlKMVC3vxb97NWL5sx146Obq2KxmthxJ4x+fbqDr5OUs2HxUD4EUKSQKRSIixVCIv40Xe9Rn1XO3MbRDTXw93NidfI7hszbTceIyZq07TPZFhSORgqRQJCJSjJX38WBkdD1W/vM2nulSh3JeVg6dzOSfc7fS/vWf+HDVAc5na241kYKgUCQiUgL4e1oZdlttVv3zNp7vHkGwnwfH0y4w9ttfafufH3ln6V7OXrAbXaZIieZmdAEiInL1vNzdePiWGjwYVY2vNhzh3aX7OHL6PK8t3sW0pft4sHVVKisbiVwXhSIRkRLIw83C/a2q0ad5GN9sPsY7S/ey70QGU5fux91sIdFrD492qI2/p9XoUkVKDF0+ExEpwawWM3c3q8KSp9rzzv1NiQjxJdth4t3lB2j32k+8u3SfxhyJXCWFIhGRUsBiNtGtUSgLHm/NQ3VyqFXBm7Tzdv6zeCftXv+JTxMO6m41kb+hUCQiUoqYTCYiyzv5blgbJvSOpEo5T06czeKFBdvpNHEZ8zYdIUcPgRTJV7EIRVOnTiU8PBybzUarVq1Yt27dX7afM2cO9erVw2az0ahRIxYtWpTn5y+99BL16tXD29ubcuXK0alTJ9auXVuYuyAiUqxYzCbublaFH5++lZfvbECQjweHT2Xy1Oxf6DZ5BUu2J2n6EJE/MTwUzZ49m9jYWMaMGcPGjRuJjIwkOjqalJSUfNuvXr2afv36MXjwYDZt2kRMTAwxMTFs27Ytt02dOnWYMmUKW7duZeXKlYSHh9OlSxdOnDhRVLslIlIsuLuZ6R8VzvJnb2VkdF38bG7sSj7LI59uoNc7q1m9N9XoEkWKDcND0cSJExkyZAiDBg2ifv36TJs2DS8vL2bMmJFv+8mTJ9O1a1dGjhxJREQE48aNo2nTpkyZMiW3zX333UenTp2oUaMGDRo0YOLEiaSnp7Nly5ai2i0RkWLFy92NoR1qseLZ23j81pp4Wi1sTjzDff9dywP/XcsviWeMLlHEcIbekp+dnc2GDRsYNWpU7jqz2UynTp1ISEjI9z0JCQnExsbmWRcdHc38+fOv+BnTp0/H39+fyMjIfNtkZWWRlZWV+zo9PR0Au92O3V6wD/y4tL2C3q78NfV70VOfG+Pv+t3LCk91rMkDLavw7rL9zFp/hJV7U1m5N5XOERV5qlMtalf0KcqSSwUd78b4c7/faP8bGopSU1PJyckhODg4z/rg4GB27tyZ73uSkpLybZ+UlJRn3Xfffce9995LZmYmoaGhxMXFERQUlO82x48fz9ixYy9bv2TJEry8vK5ll65aXFxcoWxX/pr6veipz41xNf3e3AzVG8PiI2Z+PmEibkcKP+xIpnkFJ7dXcVDeVgSFljI63o1xqd8zMzNvaDul9uGNHTp0YPPmzaSmpvL+++/Tp08f1q5dS8WKFS9rO2rUqDxnn9LT0wkLC6NLly74+fkVaF12u524uDg6d+6M1aqHqhUV9XvRU58b43r6/UFgT8o5JsXvZcmvKfx8wsTmUxbubV6Ff7SrTrCf0tHf0fFujD/3+6UrPdfL0FAUFBSExWIhOTk5z/rk5GRCQkLyfU9ISMhVtff29qZWrVrUqlWL1q1bU7t2bT744IM8l+ou8fDwwMPD47L1Vqu10A7uwty2XJn6veipz41xrf1ev3I5pvdvwS+JZ3hjyS5W7Enl07WJfLo2EV8PNyoFeBIaYCPU35NK/jZCA37/Gupvw2a1FOLelBw63o1xqd9vtO8NDUXu7u40a9aM+Ph4YmJiAHA4HMTHxzNs2LB83xMVFUV8fDwjRozIXRcXF0dUVNRffpbD4cgzbkhERC4XGRbAp4NbsXpfKhOW7GbDodOczbrIruSz7Eo+e8X3BXq7E+rvCk2VA34PS5V++xrsZ8NqMfzeHpG/ZPjls9jYWAYMGEDz5s1p2bIlkyZNIiMjg0GDBgHQv39/KleuzPjx4wEYPnw47du3Z8KECXTv3p1Zs2axfv16pk+fDkBGRgb/+te/6NmzJ6GhoaSmpjJ16lSOHj1K7969DdtPEZGSpE3NINo8FkRG1kWOp53n2JkLl309lnae42cucN6ew6mMbE5lZLP9WP6XL8wmaFwlgCduq8Vt9SpiMpmKeI9E/p7hoahv376cOHGCF198kaSkJJo0acLixYtzB1MfPnwYs/n3/120adOGmTNn8vzzzzN69Ghq167N/PnzadiwIQAWi4WdO3fy8ccfk5qaSvny5WnRogUrVqygQYMGhuyjiEhJ5e3hRq2KvtSq6Jvvz51OJ2nn7b+HpbQLHDtznuNnXN8fTztPUtoF7DlONieeYfDH64ms4s+ITnW4tW4FhSMpVgwPRQDDhg274uWypUuXXraud+/eVzzrY7PZmDt3bkGWJyIiV2AymQjwcifAy536lfK/McXhcHI8/QKfJBzkk9WH+OVIGoM++pkmYQE81bkO7WoHKRxJsaALvCIiUqjMZhOVAzwZdXsEK57rwJBbqmOzmtmceIYBM9Zx97urWbknVdOOiOEUikREpMgE+Xjwf93rs/zZDgxuWx0PNzMbD5/hgQ/W0ue9BFbv07QjYhyFIhERKXIVfW28cEd9VjzbgYFtwnF3M/PzwdPc9/5a+r6XwJr9J40uUcoghSIRETFMRT8bL/VswPKRHegfVQ13i5m1B05x7/Q13Pf+Gn4+eMroEqUMUSgSERHDhfjbePnOhiwdeSsPtK6K1WJi9b6T9J6WwAP/XcuGQwpHUvgUikREpNioFODJKzGN+OmZW+nXsipuZhMr96Zy97sJ9J+xjk2HTxtdopRiCkUiIlLsVCnnxfi7XOGob/MwLGYTy3efoNc7qxn44TpW7knlYo7D6DKllCkWzykSERHJT1igF/+5pzFDO9Ti7R/3MHfTUZbuOsHSXScI9HYnukEwtzcMJapmeU0jIjdMoUhERIq9quW9eL13JEM71GL6iv0s3pbEqYxsvliXyBfrEgnwshJdP4TbG4Vwc60gBSS5LgpFIiJSYoQHefPvXo14uWcD1h44xcKtx/nftiROZmQze30is9cn4u9ppUv9YLo1CuXmWkG4uykgydVRKBIRkRLHzWLm5lpB3FwriJd7NmDdwVMs2nqcxduSST2XxZwNR5iz4Qh+Njc61w+hW6MQ2tYOwsPNYnTpUowpFImISInmZjHTpmYQbWoGMbZnQ37+LSB9vy2JE2ez+HrjEb7eeARfmxudI4K5vVEot9QOwmZVQJK8FIpERKTUsJhNtK5RntY1yjOmRwM2HDr9W0A6TnJ6FnM3HWXupqP4eLjRMaIi3RqF0q52BTzdFZBEoUhEREopi9lEy+qBtKweyIt31Gfj4dMs3Hqc77cmkZR+gQWbj7Fg8zE83My0rRVEx4hgOkZUJNjPZnTpYhCFIhERKfXMZhPNwwNpHh7IC93rsynxzG9jkJI4euY88TtTiN+ZAvOgcRV/OtZzBaQGlfwwmUxGly9FRKFIRETKFLPZRLNq5WhWrRzPd49gZ9JZ4nck88OOFDYnnmHLkTS2HEnjzR92E+pvo2NERTpGBBNVo7zGIZVyCkUiIlJmmUwmIkL9iAj1Y9httUk5e4Gfdqbww44UVu5J5XjaBT5bc5jP1hzGy91C21pBdIoIpkO9ilTw9TC6fClgCkUiIiK/qehro2+LqvRtUZUL9hwS9p0kbkcyP+5IISn9Akt+TWbJr8mYTNAkLIBOv41DqhGocUilgUKRiIhIPmxWCx3qVaRDvYo4Y5xsP5bODzuSid+RwtajaWw6fIZNh8/w+v92UTnARk2bmbCjadxUrbzGIZVQCkUiIiJ/w2Qy0bCyPw0r+zOiUx2S0i7w484UftiRzKq9qRw9c4GjmFk+bS31Qnzp3TyMXjdVJtDb3ejS5RooFImIiFyjEH8b97Wqyn2tqnI+O4dlO5N4f8lGtp5xY2fSWcZ99yuvfr+DThHB9Gkexi21g3DTfGzFnkKRiIjIDfB0t9AxoiJZBxzc3KE9329PYc6GI2w5ksb325L4flsSwX4e3N20Cn2ahxEe5G10yXIFCkUiIiIFxN/TyoNR4TwYFc6O4+nMWX+EeZuOkJyexTtL9/HO0n20rB5In+ZhdGsUgpe7fg0XJ/rTEBERKQQRoX682KM+z91elx93pDB7fSLLd59g3YFTrDtwijELttEjshK9m4fRtGqABmcXAwpFIiIihcjDzcLtjUK5vVEox9POM3fjUb5cn8ihk5nM+jmRWT8nUrOCN32ah9GraWUq+ur2fqMoFImIiBSRUH9PhnaoxeO31mTdgVPM2XCEhVuOs+9EBuO/38lr/9tFh7oVubNJJZpVK0eov01nkIqQQpGIiEgRM5lMtKpRnlY1yvNSzwZ898sxvlyfyMbDZ/hhRzI/7EgGoIKvB5FVAmgS5k9kWACNKwfg72U1uPrSS6FIRETEQD4ebtzbsir3tqzK3pSzzFl/hJV7U9mZdJYTZ7PyhCSAGkHeRIYFEFnFFZQiQv00J1sBUSgSEREpJmpV9GVUtwgAzmfn8OvxNDYnpvFL4hl+OXKGQycz2Z+awf7UDOZtOgqA1eKavy2ySgCRYa6zSjWCfDCbddntWikUiYiIFEOe7haaVQukWbXA3HWnM7L55cgZfklM++3rGU5mZLPlSBpbjqTx6ZpDAPh6uNHotzNJ7etUoFX1QI1NugoKRSIiIiVEOW93bq1bkVvrVgTA6XRy5PT53ID0S2IaW4+mcTbrIqv3nWT1vpO8u3QfEaF+DG5bnR6RoXi46VLblSgUiYiIlFAmk4mwQC/CAr24o3ElAC7mONiTco5fEs/w88HTLNp6nB3H03lmzi/8Z/FOHmxdjftbVaW8j4fB1Rc/CkUiIiKliJvFTESoHxGhftzbsiov3BHBzHWH+WT1IZLSLzAxbjdTf9pLr5sq81Db6tQJ9jW65GJDs9OJiIiUYgFe7jx+ay1WPNeByfc2oXEVf7IuOpj1cyJd3lzOgx+sZemuFJxOp9GlGk5nikRERMoAq8XMnU0q0zOyEusPneaDFQdY8msSK/aksmJPKrUq+vDQzdW5q2nlAr/F3+FwcvBkBtuPpbP9WDq/Hk+nT/MquZf8ioticaZo6tSphIeHY7PZaNWqFevWrfvL9nPmzKFevXrYbDYaNWrEokWLcn9mt9t57rnnaNSoEd7e3lSqVIn+/ftz7Nixwt4NERGRYs9kMtEiPJBpDzZj2cgOPHRzdXw83Nibco7R87YSNT6eCUt2kZJ+4bq2n3Uxh61H0pj982FeXLCNu99dTcOX/sdtE5bxxBebmLZsH8t3n2D9wdMFvGc3zvAzRbNnzyY2NpZp06bRqlUrJk2aRHR0NLt27aJixYqXtV+9ejX9+vVj/Pjx3HHHHcycOZOYmBg2btxIw4YNyczMZOPGjbzwwgtERkZy+vRphg8fTs+ePVm/fr0BeygiIlI8hQV68WKP+jzVuTazf07ko9UHOXL6PG//uJdpy/bRI7ISg9tWp0El/3zfn37Bzq+Xzv4cS2f7sTT2ppzjouPyS3EebmbqhfrRoJJraREemM8WjWV4KJo4cSJDhgxh0KBBAEybNo2FCxcyY8YM/vnPf17WfvLkyXTt2pWRI0cCMG7cOOLi4pgyZQrTpk3D39+fuLi4PO+ZMmUKLVu25PDhw1StWrXwd0pERKQE8bVZefiWGgxsE07cr8l8sPIA6w+dZu7Go8zdeJTWNQIZdHN1rBYT24/+fgns8KnMfLcX4GX9Lfz4U/+3IFQ9yBs3S7G4QHVFhoai7OxsNmzYwKhRo3LXmc1mOnXqREJCQr7vSUhIIDY2Ns+66Oho5s+ff8XPSUtLw2QyERAQkO/Ps7KyyMrKyn2dnp4OuC7F2e32q9ybq3NpewW9Xflr6veipz43hvrdGKWp3zvVC6JTvSB+OZLGh6sPsXh7Mmv2n2LN/lP5tq8cYCMixJf6oX7UD/UlItQ334lsnY4c7I6cAq31z/1+o/1vaChKTU0lJyeH4ODgPOuDg4PZuXNnvu9JSkrKt31SUlK+7S9cuMBzzz1Hv3798PPzy7fN+PHjGTt27GXrlyxZgpeX19XsyjX789ksKRrq96KnPjeG+t0Ypa3fu/hAiyawIsnMhlQTNgtU8XZS2dtJFW+o7OXE23oOOAcXjpN1ADYfgM1FXOelfs/MzP/M1dUy/PJZYbLb7fTp0wen08m77757xXajRo3Kc/YpPT2dsLAwunTpcsUgdSM1xcXF0blzZ6xWzXRcVNTvRU99bgz1uzFKe7/fb3QBV/Dnfr90ped6GRqKgoKCsFgsJCcn51mfnJxMSEhIvu8JCQm5qvaXAtGhQ4f48ccf/zLceHh44OFx+ZM9rVZroR3chbltuTL1e9FTnxtD/W4M9bsxLvX7jfa9oSOe3N3dadasGfHx8bnrHA4H8fHxREVF5fueqKioPO3Bddrsj+0vBaI9e/bwww8/UL58+cLZARERESk1DL98Fhsby4ABA2jevDktW7Zk0qRJZGRk5N6N1r9/fypXrsz48eMBGD58OO3bt2fChAl0796dWbNmsX79eqZPnw64AtE999zDxo0b+e6778jJyckdbxQYGIi7u7sxOyoiIiLFmuGhqG/fvpw4cYIXX3yRpKQkmjRpwuLFi3MHUx8+fBiz+fcTWm3atGHmzJk8//zzjB49mtq1azN//nwaNmwIwNGjR/nmm28AaNKkSZ7P+umnn7j11luLZL9ERESkZDE8FAEMGzaMYcOG5fuzpUuXXraud+/e9O7dO9/24eHhmr9FRERErlnxfoqSiIiISBFRKBIRERFBoUhEREQEUCgSERERARSKRERERACFIhERERFAoUhEREQEUCgSERERARSKRERERIBi8kTr4ubSE7HT09MLfNt2u53MzEzS09M1k3IRUr8XPfW5MdTvxlC/G+PP/X7p9/b1zmyhUJSPs2fPAhAWFmZwJSIiInKtzp49i7+//zW/z+TURGGXcTgcHDt2DF9fX0wmU4FuOz09nbCwMBITE/Hz8yvQbcuVqd+LnvrcGOp3Y6jfjfHnfnc6nZw9e5ZKlSrlmUz+aulMUT7MZjNVqlQp1M/w8/PTXxwDqN+LnvrcGOp3Y6jfjfHHfr+eM0SXaKC1iIiICApFIiIiIoBCUZHz8PBgzJgxeHh4GF1KmaJ+L3rqc2Oo342hfjdGQfe7BlqLiIiIoDNFIiIiIoBCkYiIiAigUCQiIiICKBSJiIiIAApFRWrq1KmEh4djs9lo1aoV69atM7qkUu2ll17CZDLlWerVq2d0WaXO8uXL6dGjB5UqVcJkMjF//vw8P3c6nbz44ouEhobi6elJp06d2LNnjzHFliJ/1+8DBw687Pjv2rWrMcWWIuPHj6dFixb4+vpSsWJFYmJi2LVrV542Fy5cYOjQoZQvXx4fHx/uvvtukpOTDaq4dLiafr/11lsvO+YfffTRa/ochaIiMnv2bGJjYxkzZgwbN24kMjKS6OhoUlJSjC6tVGvQoAHHjx/PXVauXGl0SaVORkYGkZGRTJ06Nd+fv/baa7z11ltMmzaNtWvX4u3tTXR0NBcuXCjiSkuXv+t3gK5du+Y5/r/44osirLB0WrZsGUOHDmXNmjXExcVht9vp0qULGRkZuW2eeuopvv32W+bMmcOyZcs4duwYd911l4FVl3xX0+8AQ4YMyXPMv/baa9f2QU4pEi1btnQOHTo093VOTo6zUqVKzvHjxxtYVek2ZswYZ2RkpNFllCmAc968ebmvHQ6HMyQkxPn666/nrjtz5ozTw8PD+cUXXxhQYen05353Op3OAQMGOO+8805D6ilLUlJSnIBz2bJlTqfTdXxbrVbnnDlzctvs2LHDCTgTEhKMKrPU+XO/O51OZ/v27Z3Dhw+/oe3qTFERyM7OZsOGDXTq1Cl3ndlsplOnTiQkJBhYWem3Z88eKlWqRI0aNbj//vs5fPiw0SWVKQcOHCApKSnPse/v70+rVq107BeBpUuXUrFiRerWrctjjz3GyZMnjS6p1ElLSwMgMDAQgA0bNmC32/Mc8/Xq1aNq1ao65gvQn/v9ks8//5ygoCAaNmzIqFGjyMzMvKbtakLYIpCamkpOTg7BwcF51gcHB7Nz506Dqir9WrVqxUcffUTdunU5fvw4Y8eO5ZZbbmHbtm34+voaXV6ZkJSUBJDvsX/pZ1I4unbtyl133UX16tXZt28fo0eP5vbbbychIQGLxWJ0eaWCw+FgxIgR3HzzzTRs2BBwHfPu7u4EBATkaatjvuDk1+8A9913H9WqVaNSpUps2bKF5557jl27djF37tyr3rZCkZRat99+e+73jRs3plWrVlSrVo0vv/ySwYMHG1iZSOG79957c79v1KgRjRs3pmbNmixdupSOHTsaWFnpMXToULZt26axikXsSv3+yCOP5H7fqFEjQkND6dixI/v27aNmzZpXtW1dPisCQUFBWCyWy+4+SE5OJiQkxKCqyp6AgADq1KnD3r17jS6lzLh0fOvYN16NGjUICgrS8V9Ahg0bxnfffcdPP/1ElSpVcteHhISQnZ3NmTNn8rTXMV8wrtTv+WnVqhXANR3zCkVFwN3dnWbNmhEfH5+7zuFwEB8fT1RUlIGVlS3nzp1j3759hIaGGl1KmVG9enVCQkLyHPvp6emsXbtWx34RO3LkCCdPntTxf4OcTifDhg1j3rx5/Pjjj1SvXj3Pz5s1a4bVas1zzO/atYvDhw/rmL8Bf9fv+dm8eTPANR3zunxWRGJjYxkwYADNmzenZcuWTJo0iYyMDAYNGmR0aaXWM888Q48ePahWrRrHjh1jzJgxWCwW+vXrZ3Rppcq5c+fy/E/swIEDbN68mcDAQKpWrcqIESN45ZVXqF27NtWrV+eFF16gUqVKxMTEGFd0KfBX/R4YGMjYsWO5++67CQkJYd++fTz77LPUqlWL6OhoA6su+YYOHcrMmTNZsGABvr6+ueOE/P398fT0xN/fn8GDBxMbG0tgYCB+fn488cQTREVF0bp1a4OrL7n+rt/37dvHzJkz6datG+XLl2fLli089dRTtGvXjsaNG1/9B93QvWtyTd5++21n1apVne7u7s6WLVs616xZY3RJpVrfvn2doaGhTnd3d2flypWdffv2de7du9foskqdn376yQlctgwYMMDpdLpuy3/hhRecwcHBTg8PD2fHjh2du3btMrboUuCv+j0zM9PZpUsXZ4UKFZxWq9VZrVo155AhQ5xJSUlGl13i5dfngPPDDz/MbXP+/Hnn448/7ixXrpzTy8vL2atXL+fx48eNK7oU+Lt+P3z4sLNdu3bOwMBAp4eHh7NWrVrOkSNHOtPS0q7pc0y/fZiIiIhImaYxRSIiIiIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiMhVWbp0KSaT6bI5rUSk9FAoEhEREUGhSERERARQKBKREsLhcDB+/HiqV6+Op6cnkZGRfPXVV8Dvl7YWLlxI48aNsdlstG7dmm3btuXZxtdff02DBg3w8PAgPDycCRMm5Pl5VlYWzz33HGFhYXh4eFCrVi0++OCDPG02bNhA8+bN8fLyok2bNuzatatwd1xEioxCkYiUCOPHj+eTTz5h2rRpbN++naeeeooHHniAZcuW5bYZOXIkEyZM4Oeff6ZChQr06NEDu90OuMJMnz59uPfee9m6dSsvvfQSL7zwAh999FHu+/v3788XX3zBW2+9xY4dO3jvvffw8fHJU8f//d//MWHCBNavX4+bmxsPPfRQkey/iBQ+TQgrIsVeVlYWgYGB/PDDD0RFReWuf/jhh8nMzOSRRx6hQ4cOzJo1i759+wJw6tQpqlSpwkcffUSfPn24//77OXHiBEuWLMl9/7PPPsvChQvZvn07u3fvpm7dusTFxdGpU6fLali6dCkdOnTghx9+oGPHjgAsWrSI7t27c/78eWw2WyH3gogUNp0pEpFib+/evWRmZtK5c2d8fHxyl08++YR9+/bltvtjYAoMDKRu3brs2LEDgB07dnDzzTfn2e7NN9/Mnj17yMnJYfPmzVgsFtq3b/+XtTRu3Dj3+9DQUABSUlJueB9FxHhuRhcgIvJ3zp07B8DChQupXLlynp95eHjkCUbXy9PT86raWa3W3O9NJhPgGu8kIiWfzhSJSLFXv359PDw8OHz4MLVq1cqzhIWF5bZbs2ZN7venT59m9+7dREREABAREcGqVavybHfVqlXUqVMHi8VCo0aNcDgcecYoiUjZojNFIlLs+fr68swzz/DUU0/hcDho27YtaWlprFq1Cj8/P6pVqwbAyy+/TPny5QkODub//u//CAoKIiYmBoCnn36aFi1aMG7cOPr27UtCQgJTpkzhnXfeASA8PJwBAwbw0EMP8dZbbxEZGcmhQ4dISUmhT58+Ru26iBQhhSIRKRHGjRtHhQoVGD9+PPv37ycgIICmTZsyevTo3MtXr776KsOHD2fPnj00adKEb7/9Fnd3dwCaNm3Kl19+yYsvvsi4ceMIDQ3l5ZdfZuDAgbmf8e677zJ69Ggef/xxTp48SdWqVRk9erQRuysiBtDdZyJS4l26M+z06dMEBAQYXY6IlFAaUyQiIiKCQpGIiIgIoMtnIiIiIoDOFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgACkUiIiIigEKRiIiICKBQJCIiIgIoFImIiIgA8P/TDoB85OGjOAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "RoVAdJyW-fvB",
        "outputId": "cf416c66-05d2-46a6-e520-db24ee6bfcec"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history, 'accuracy', 'val_accuracy', 'Accuracy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "YT6wyQ6R-fvB",
        "outputId": "3802ccb5-d564-4c24-addd-d404bb4058c9"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history, 'precision', 'val_precision', 'Precision')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "6-FtjDjn-fvB",
        "outputId": "1256b57e-950e-4f74-d679-4538f88f6a73"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "history_results(history, 'recall', 'val_recall', 'Recall')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xNTGAR0VDUIs",
        "outputId": "640b717a-602b-4704-adf2-c6f5e7b1c8e9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m190/190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m65s\u001b[0m 263ms/step - accuracy: 0.4084 - loss: 0.6931 - precision: 0.5916 - recall: 1.0000\n"
          ]
        }
      ],
      "source": [
        "# before training\n",
        "print(loss0, accuracy0, precision0, recall0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "outputId": "4a1bc771-c602-4e8b-daf1-65288b8d2392",
        "id": "sUox-5KOVe_V"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m190/190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 257ms/step - accuracy: 0.9606 - loss: 0.1174 - precision: 0.9585 - recall: 0.9785\n"
          ]
        }
      ],
      "source": [
        "# after 25 epochs\n",
        "print(loss_test, accuracy_test, precision_test, recall_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aXwgbvsR-fvC",
        "outputId": "1f06bf6b-c7cb-4bd2-a451-cfa73ab8972d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m190/190\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m47s\u001b[0m 244ms/step - accuracy: 0.9753 - loss: 0.0656 - precision: 0.9776 - recall: 0.9853\n"
          ]
        }
      ],
      "source": [
        "# after 50 epochs\n",
        "loss_test, accuracy_test, precision_test, recall_test = detector.evaluate(test_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpJ2Cq0Jz15T"
      },
      "source": [
        "## TESTING\n",
        "\n",
        "Now we are testing our model on fully unseen 1080p pixelated images\n",
        "- `un_mod_ultimate.zip` is a subset (1028 images) of unsplash dataset, pixelated and cropped to 1920x1080"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SeVhDK4xz15a"
      },
      "outputs": [],
      "source": [
        "!unzip /content/drive/MyDrive/ULTIMATE_DATASET/un_mod_ultimate.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8aNuI9qAz15b"
      },
      "outputs": [],
      "source": [
        "!mkdir un_mod_ultimate\n",
        "\n",
        "!mv /content/x8/* /content/un_mod_ultimate/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2sTyZ-oaz15b",
        "outputId": "b5a36de1-8ecc-4951-8538-de56bf27a877"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "1028"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(os.listdir('./un_mod_ultimate/'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TUZSEuY8DUIs"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow.keras as tfk\n",
        "from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score, precision_score, recall_score, f1_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZaveuEE8z15b"
      },
      "outputs": [],
      "source": [
        "BATCH_SIZE = 4 # use  1 if test images are of different sizes\n",
        "                # use >1 if all the images are of same size\n",
        "\n",
        "\n",
        "NON_PIXELATED = 0\n",
        "PIXELATED = 1\n",
        "class_names = ['Non-Pixelated', 'Pixelated']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBe3wve6z15b",
        "outputId": "11ba07cc-fc03-4691-8967-15f5281c9b99"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1028\n",
            "1028\n",
            "['./un_mod_ultimate/800001019_d3.png', './un_mod_ultimate/800001020_d8.png', './un_mod_ultimate/800001021_d2.png', './un_mod_ultimate/800001022_real.png', './un_mod_ultimate/800001023_real.png', './un_mod_ultimate/800001024_d6.png', './un_mod_ultimate/800001025_d3.png', './un_mod_ultimate/800001026_j10.png', './un_mod_ultimate/800001027_d6.png', './un_mod_ultimate/800001028_d2.png']\n",
            "[1, 1, 1, 0, 0, 1, 1, 1, 1, 1]\n"
          ]
        }
      ],
      "source": [
        "# implement the cell to get the image_paths and image_labels lists\n",
        "# image_paths : this contains all the image paths eg. ['./path/to/image1.jpg', './path/to/image2.png', ...]\n",
        "# image_labels: this containts the labels (same type as PIXELATED, NON_PIXELATED variables) eg. [True, False, False, ...]\n",
        "# save all the test images in a single image_dir\n",
        "\n",
        "image_dir = './un_mod_ultimate/'\n",
        "image_basenames = sorted(os.listdir(image_dir))\n",
        "\n",
        "image_paths = [os.path.join(image_dir, bname) for bname in image_basenames]\n",
        "image_labels = [NON_PIXELATED if bname.split('_')[1][0] == 'r' else PIXELATED for bname in image_basenames]\n",
        "\n",
        "\n",
        "# check if your implementation working correctly\n",
        "\n",
        "print(len(image_paths))\n",
        "print(len(image_labels))\n",
        "\n",
        "print(image_paths[-10:])\n",
        "print(image_labels[-10:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "epjr7tDJz15b"
      },
      "outputs": [],
      "source": [
        "# this function loads the image from corresponding image_path and preprocesses it to have:\n",
        "# - pixel values in [0,1], float32 type, 3 channels\n",
        "# - pad to have height, width as multiple of 64 (this is required due to model architecture)\n",
        "\n",
        "@tf.function\n",
        "def load_process_image(image_path):\n",
        "    img = tf.io.read_file(image_path)\n",
        "    img = tf.image.decode_image(img, channels=3, expand_animations=False, dtype=tf.float32)\n",
        "\n",
        "    shape = tf.shape(img)\n",
        "    height, width = shape[0], shape[1]\n",
        "    padding_width  = ((width  + 63) // 64) * 64 - width\n",
        "    padding_height = ((height + 63) // 64) * 64 - height\n",
        "    left  = padding_width  // 2\n",
        "    upper = padding_height // 2\n",
        "\n",
        "    return tf.image.pad_to_bounding_box(img, upper, left, height + padding_height, width + padding_width)\n",
        "\n",
        "\n",
        "# create the dataset, batch, prefetch object\n",
        "\n",
        "custom_test_dataset = tf.data.Dataset.from_tensor_slices(image_paths).map(load_process_image, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                                                                     .batch(batch_size=BATCH_SIZE, num_parallel_calls=tf.data.AUTOTUNE, deterministic=True)\\\n",
        "                                                                     .prefetch(buffer_size=tf.data.AUTOTUNE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8V24ZJElz15b",
        "outputId": "e2ffc3d0-27a7-4554-ab6b-e2facd7c6283"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[1m257/257\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m114s\u001b[0m 374ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = detector.predict(custom_test_dataset)\n",
        "pred_labels = tf.where(predictions < 0, NON_PIXELATED, PIXELATED).numpy().flatten()\n",
        "true_labels = np.array(image_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gkYmeFx3z15c",
        "outputId": "7cf0b21d-a2e2-40f4-ea8a-9ced663347c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Accuracy: 0.8716\n",
            "\n",
            "Class Names    Precision  Recall  F1-Score\n",
            "--------------------------------------------\n",
            "Non-Pixelated    0.8096   0.8781   0.8425\n",
            "Pixelated        0.9172   0.8674   0.8916\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "accuracy = accuracy_score(true_labels, pred_labels)\n",
        "precision = precision_score(true_labels, pred_labels, average=None, zero_division=0.0)\n",
        "recall = recall_score(true_labels, pred_labels, average=None, zero_division=0.0)\n",
        "f1 = f1_score(true_labels, pred_labels, average=None)\n",
        "\n",
        "print(f\"\\nAccuracy: {accuracy:.4f}\")\n",
        "print(\"\\nClass Names    Precision  Recall  F1-Score\")\n",
        "print(\"--------------------------------------------\")\n",
        "for class_name, p, r, f in zip(class_names, precision, recall, f1):\n",
        "  print(f\"{class_name:15}  {p:.4f}   {r:.4f}   {f:.4f}\")\n",
        "\n",
        "_ = ConfusionMatrixDisplay.from_predictions(true_labels, pred_labels, display_labels=class_names, normalize='true', cmap='Blues')\n",
        "_ = ConfusionMatrixDisplay.from_predictions(true_labels, pred_labels, display_labels=class_names, normalize=None,   cmap='Blues')\n",
        "_ = ConfusionMatrixDisplay.from_predictions(true_labels, pred_labels, display_labels=class_names, normalize='all',  cmap='Blues')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KXj4qCpvULxc"
      },
      "outputs": [],
      "source": [
        "from google.colab import runtime\n",
        "runtime.unassign()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.4"
    }
  },
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