{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Using RoboFlow dataset to detect people using MediaPipe\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KrpKTU4pQPn3",
        "outputId": "8e2fe2ab-89b8-404a-bb51-e5a840a27f83"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: seqeval\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: typeguard, tensorflow-model-optimization, protobuf, portalocker, immutabledict, colorama, tensorflow-addons, sounddevice, sacrebleu, tensorflow-metadata, seqeval, mediapipe, tensorflow-text, tf-models-official, mediapipe-model-maker\n",
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            "    Found existing installation: protobuf 3.20.3\n",
            "    Uninstalling protobuf-3.20.3:\n",
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            "      Successfully uninstalled tensorflow-metadata-1.15.0\n",
            "Successfully installed colorama-0.4.6 immutabledict-4.2.0 mediapipe-0.10.14 mediapipe-model-maker-0.2.1.4 portalocker-2.8.2 protobuf-4.25.3 sacrebleu-2.4.2 seqeval-1.2.2 sounddevice-0.4.6 tensorflow-addons-0.23.0 tensorflow-metadata-1.13.1 tensorflow-model-optimization-0.7.5 tensorflow-text-2.15.0 tf-models-official-2.15.0 typeguard-2.13.3\n"
          ]
        }
      ],
      "source": [
        "!pip install 'keras<3.0.0' mediapipe-model-maker"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Import libraies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dH9J0lzVQSnL",
        "outputId": "c4af51cf-41f5-485f-b818-3651037a132c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning: \n",
            "\n",
            "TensorFlow Addons (TFA) has ended development and introduction of new features.\n",
            "TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.\n",
            "Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP). \n",
            "\n",
            "For more information see: https://github.com/tensorflow/addons/issues/2807 \n",
            "\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import tensorflow as tf\n",
        "assert tf.__version__.startswith('2')\n",
        "from google.colab import files\n",
        "\n",
        "from mediapipe_model_maker import object_detector"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Download dataset directly from Roboflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GDAUMQ2WQWbT"
      },
      "outputs": [],
      "source": [
        "!pip install roboflow\n",
        "\n",
        "from roboflow import Roboflow\n",
        "rf = Roboflow(api_key=\"rda3pff5KNqzUs0cUoSA\")\n",
        "project = rf.workspace(\"yolodataset\").project(\"person-detection-9a6mk\")\n",
        "version = project.version(1)\n",
        "dataset = version.download(\"coco\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Loading, training, and evaluating"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "CYVbjwX9QZwE"
      },
      "outputs": [],
      "source": [
        "class TransferLearning():\n",
        "    \"\"\" Transfer Learning class for object detection using MediaPipe Model Maker\n",
        "    2 datasets are required: train and validation\n",
        "\n",
        "    Attributes:\n",
        "        train_dataset_path: path to the train dataset\n",
        "        validation_dataset_path: path to the validation dataset\n",
        "        model: model to be trained\n",
        "        train_data: train dataset\n",
        "        validation_data: validation dataset\n",
        "    \"\"\"\n",
        "    def __init__(self):\n",
        "        self.train_dataset_path = \"/content/drive/MyDrive/PeopleDetection/train\"\n",
        "        self.validation_dataset_path = \"/content/drive/MyDrive/PeopleDetection/valid\"\n",
        "        self.model = None\n",
        "        self.train_data = None\n",
        "        self.validation_data = None\n",
        "        self.hparams = None\n",
        "        self.options = None\n",
        "\n",
        "    def load_data(self):\n",
        "        \"\"\" Load train and validation datasets from the given paths        \"\"\"\n",
        "        self.train_data = object_detector.Dataset.from_pascal_voc_folder(self.train_dataset_path)\n",
        "        self.validation_data = object_detector.Dataset.from_pascal_voc_folder(self.validation_dataset_path)\n",
        "\n",
        "    def train_model(self,batch_size=8, learning_rate=0.1, epochs=100, export_dir='exported_model'):\n",
        "        \"\"\" Train the model using the loaded datasets\n",
        "\n",
        "        Args:\n",
        "            batch_size: batch size for training\n",
        "            learning_rate: learning rate for training\n",
        "            epochs: number of epochs for training\n",
        "            export_dir: directory to export the trained model\n",
        "        \"\"\"\n",
        "        self.hparams = object_detector.HParams(batch_size=batch_size, learning_rate=learning_rate, epochs=epochs, export_dir=export_dir)\n",
        "        self.options = object_detector.ObjectDetectorOptions(\n",
        "            supported_model=object_detector.SupportedModels.MOBILENET_V2,\n",
        "            hparams=self.hparams\n",
        "        )\n",
        "\n",
        "        self.model = object_detector.ObjectDetector.create(\n",
        "            train_data=self.train_data,\n",
        "            validation_data=self.validation_data,\n",
        "            options=self.options)\n",
        "\n",
        "    def evaluate_model(self,batch_size=8):\n",
        "        \"\"\" Evaluate the trained model\n",
        "\n",
        "        Args:\n",
        "            batch_size: batch size for evaluation\n",
        "        \"\"\"\n",
        "        loss, metrics = self.model.evaluate(self.validation_data,batch_size)\n",
        "        print(f\"Validation loss: {loss}\")\n",
        "        print(f\"Validation metrics: {metrics}\")\n",
        "\n",
        "    def export_model(self):\n",
        "        \"\"\" Export the trained model \"\"\"\n",
        "        self.model.export_model('people-detection.tflite')\n",
        "        print(f\"Model exported\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PkAxfZ_rQeXG"
      },
      "outputs": [],
      "source": [
        "TransferLearning_model = TransferLearning()\n",
        "TransferLearning_model.load_data()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cgr2Hdu7VHem",
        "outputId": "c110e087-43bf-42a2-f04e-5b7ef594d8e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Using existing files at /tmp/model_maker/object_detector/mobilenetv2_i256\n",
            "Model: \"retina_net_model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " mobile_net_1 (MobileNet)    {'2': (None, 64, 64, 24   2257984   \n",
            "                             ),                                  \n",
            "                              '3': (None, 32, 32, 32             \n",
            "                             ),                                  \n",
            "                              '4': (None, 16, 16, 96             \n",
            "                             ),                                  \n",
            "                              '5': (None, 8, 8, 320)             \n",
            "                             , '6': (None, 8, 8, 128             \n",
            "                             0)}                                 \n",
            "                                                                 \n",
            " fpn_1 (FPN)                 {'5': (None, 8, 8, 128)   149056    \n",
            "                             , '4': (None, 16, 16, 1             \n",
            "                             28),                                \n",
            "                              '3': (None, 32, 32, 12             \n",
            "                             8),                                 \n",
            "                              '6': (None, 4, 4, 128)             \n",
            "                             , '7': (None, 2, 2, 128             \n",
            "                             )}                                  \n",
            "                                                                 \n",
            " multilevel_detection_gener  multiple                  0 (unused)\n",
            " ator_1 (MultilevelDetectio                                      \n",
            " nGenerator)                                                     \n",
            "                                                                 \n",
            " retina_net_head_1 (RetinaN  ({'3': (None, 32, 32, 1   171062    \n",
            " etHead)                     8),                                 \n",
            "                              '4': (None, 16, 16, 18             \n",
            "                             ),                                  \n",
            "                              '5': (None, 8, 8, 18),             \n",
            "                              '6': (None, 4, 4, 18),             \n",
            "                              '7': (None, 2, 2, 18)}             \n",
            "                             , {'3': (None, 32, 32,              \n",
            "                             36),                                \n",
            "                              '4': (None, 16, 16, 36             \n",
            "                             ),                                  \n",
            "                              '5': (None, 8, 8, 36),             \n",
            "                              '6': (None, 4, 4, 36),             \n",
            "                              '7': (None, 2, 2, 36)}             \n",
            "                             , {})                               \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2578102 (9.83 MB)\n",
            "Trainable params: 2532470 (9.66 MB)\n",
            "Non-trainable params: 45632 (178.25 KB)\n",
            "_________________________________________________________________\n",
            "Epoch 1/100\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_4/batch_normalization/gamma:0', 'conv2dbn_block_4/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_4/batch_normalization/gamma:0', 'conv2dbn_block_4/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_4/batch_normalization/gamma:0', 'conv2dbn_block_4/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n",
            "WARNING:tensorflow:Gradients do not exist for variables ['conv2dbn_block_4/batch_normalization/gamma:0', 'conv2dbn_block_4/batch_normalization/beta:0'] when minimizing the loss. If you're using `model.compile()`, did you forget to provide a `loss` argument?\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "58/58 [==============================] - 56s 252ms/step - total_loss: 1.6840 - cls_loss: 1.4160 - box_loss: 0.0043 - model_loss: 1.6291 - val_total_loss: 1.3220 - val_cls_loss: 1.1915 - val_box_loss: 0.0015 - val_model_loss: 1.2671\n",
            "Epoch 2/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 1.3179 - cls_loss: 1.1044 - box_loss: 0.0032 - model_loss: 1.2630 - val_total_loss: 1.0755 - val_cls_loss: 0.9413 - val_box_loss: 0.0016 - val_model_loss: 1.0205\n",
            "Epoch 3/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 1.0288 - cls_loss: 0.8628 - box_loss: 0.0022 - model_loss: 0.9738 - val_total_loss: 0.8156 - val_cls_loss: 0.6887 - val_box_loss: 0.0014 - val_model_loss: 0.7607\n",
            "Epoch 4/100\n",
            "58/58 [==============================] - 10s 164ms/step - total_loss: 0.7748 - cls_loss: 0.6346 - box_loss: 0.0017 - model_loss: 0.7198 - val_total_loss: 0.6834 - val_cls_loss: 0.5618 - val_box_loss: 0.0013 - val_model_loss: 0.6285\n",
            "Epoch 5/100\n",
            "58/58 [==============================] - 10s 174ms/step - total_loss: 0.5706 - cls_loss: 0.4493 - box_loss: 0.0013 - model_loss: 0.5156 - val_total_loss: 0.5903 - val_cls_loss: 0.4719 - val_box_loss: 0.0013 - val_model_loss: 0.5354\n",
            "Epoch 6/100\n",
            "58/58 [==============================] - 10s 172ms/step - total_loss: 0.4468 - cls_loss: 0.3364 - box_loss: 0.0011 - model_loss: 0.3918 - val_total_loss: 0.5029 - val_cls_loss: 0.3826 - val_box_loss: 0.0013 - val_model_loss: 0.4479\n",
            "Epoch 7/100\n",
            "58/58 [==============================] - 9s 152ms/step - total_loss: 0.3687 - cls_loss: 0.2690 - box_loss: 8.9543e-04 - model_loss: 0.3138 - val_total_loss: 0.4401 - val_cls_loss: 0.3230 - val_box_loss: 0.0012 - val_model_loss: 0.3851\n",
            "Epoch 8/100\n",
            "58/58 [==============================] - 10s 166ms/step - total_loss: 0.3224 - cls_loss: 0.2254 - box_loss: 8.4212e-04 - model_loss: 0.2675 - val_total_loss: 0.4191 - val_cls_loss: 0.3006 - val_box_loss: 0.0013 - val_model_loss: 0.3641\n",
            "Epoch 9/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.2824 - cls_loss: 0.1937 - box_loss: 6.7377e-04 - model_loss: 0.2274 - val_total_loss: 0.3825 - val_cls_loss: 0.2630 - val_box_loss: 0.0013 - val_model_loss: 0.3276\n",
            "Epoch 10/100\n",
            "58/58 [==============================] - 10s 173ms/step - total_loss: 0.2482 - cls_loss: 0.1637 - box_loss: 5.9065e-04 - model_loss: 0.1932 - val_total_loss: 0.3733 - val_cls_loss: 0.2519 - val_box_loss: 0.0013 - val_model_loss: 0.3184\n",
            "Epoch 11/100\n",
            "58/58 [==============================] - 9s 153ms/step - total_loss: 0.2254 - cls_loss: 0.1436 - box_loss: 5.3736e-04 - model_loss: 0.1705 - val_total_loss: 0.3483 - val_cls_loss: 0.2275 - val_box_loss: 0.0013 - val_model_loss: 0.2934\n",
            "Epoch 12/100\n",
            "58/58 [==============================] - 11s 179ms/step - total_loss: 0.2134 - cls_loss: 0.1337 - box_loss: 4.9445e-04 - model_loss: 0.1584 - val_total_loss: 0.3503 - val_cls_loss: 0.2308 - val_box_loss: 0.0013 - val_model_loss: 0.2954\n",
            "Epoch 13/100\n",
            "58/58 [==============================] - 10s 170ms/step - total_loss: 0.2008 - cls_loss: 0.1197 - box_loss: 5.2362e-04 - model_loss: 0.1459 - val_total_loss: 0.3455 - val_cls_loss: 0.2242 - val_box_loss: 0.0013 - val_model_loss: 0.2906\n",
            "Epoch 14/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.1877 - cls_loss: 0.1109 - box_loss: 4.3546e-04 - model_loss: 0.1327 - val_total_loss: 0.3372 - val_cls_loss: 0.2169 - val_box_loss: 0.0013 - val_model_loss: 0.2823\n",
            "Epoch 15/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1726 - cls_loss: 0.0985 - box_loss: 3.8342e-04 - model_loss: 0.1177 - val_total_loss: 0.3672 - val_cls_loss: 0.2423 - val_box_loss: 0.0014 - val_model_loss: 0.3123\n",
            "Epoch 16/100\n",
            "58/58 [==============================] - 9s 151ms/step - total_loss: 0.1703 - cls_loss: 0.0962 - box_loss: 3.8260e-04 - model_loss: 0.1154 - val_total_loss: 0.3559 - val_cls_loss: 0.2375 - val_box_loss: 0.0013 - val_model_loss: 0.3010\n",
            "Epoch 17/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1692 - cls_loss: 0.0941 - box_loss: 4.0279e-04 - model_loss: 0.1143 - val_total_loss: 0.3512 - val_cls_loss: 0.2347 - val_box_loss: 0.0012 - val_model_loss: 0.2962\n",
            "Epoch 18/100\n",
            "58/58 [==============================] - 10s 174ms/step - total_loss: 0.1515 - cls_loss: 0.0808 - box_loss: 3.1526e-04 - model_loss: 0.0966 - val_total_loss: 0.3413 - val_cls_loss: 0.2211 - val_box_loss: 0.0013 - val_model_loss: 0.2864\n",
            "Epoch 19/100\n",
            "58/58 [==============================] - 9s 155ms/step - total_loss: 0.1478 - cls_loss: 0.0760 - box_loss: 3.3900e-04 - model_loss: 0.0929 - val_total_loss: 0.3312 - val_cls_loss: 0.2146 - val_box_loss: 0.0012 - val_model_loss: 0.2763\n",
            "Epoch 20/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.1476 - cls_loss: 0.0749 - box_loss: 3.5538e-04 - model_loss: 0.0927 - val_total_loss: 0.3337 - val_cls_loss: 0.2179 - val_box_loss: 0.0012 - val_model_loss: 0.2788\n",
            "Epoch 21/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.1448 - cls_loss: 0.0719 - box_loss: 3.5935e-04 - model_loss: 0.0899 - val_total_loss: 0.3377 - val_cls_loss: 0.2228 - val_box_loss: 0.0012 - val_model_loss: 0.2828\n",
            "Epoch 22/100\n",
            "58/58 [==============================] - 9s 155ms/step - total_loss: 0.1327 - cls_loss: 0.0636 - box_loss: 2.8470e-04 - model_loss: 0.0778 - val_total_loss: 0.3492 - val_cls_loss: 0.2320 - val_box_loss: 0.0012 - val_model_loss: 0.2943\n",
            "Epoch 23/100\n",
            "58/58 [==============================] - 10s 167ms/step - total_loss: 0.1315 - cls_loss: 0.0617 - box_loss: 2.9893e-04 - model_loss: 0.0766 - val_total_loss: 0.3484 - val_cls_loss: 0.2308 - val_box_loss: 0.0013 - val_model_loss: 0.2935\n",
            "Epoch 24/100\n",
            "58/58 [==============================] - 11s 182ms/step - total_loss: 0.1334 - cls_loss: 0.0627 - box_loss: 3.1571e-04 - model_loss: 0.0785 - val_total_loss: 0.3256 - val_cls_loss: 0.2088 - val_box_loss: 0.0012 - val_model_loss: 0.2707\n",
            "Epoch 25/100\n",
            "58/58 [==============================] - 11s 179ms/step - total_loss: 0.1261 - cls_loss: 0.0573 - box_loss: 2.7877e-04 - model_loss: 0.0712 - val_total_loss: 0.3409 - val_cls_loss: 0.2243 - val_box_loss: 0.0012 - val_model_loss: 0.2860\n",
            "Epoch 26/100\n",
            "58/58 [==============================] - 10s 173ms/step - total_loss: 0.1251 - cls_loss: 0.0553 - box_loss: 2.9841e-04 - model_loss: 0.0702 - val_total_loss: 0.3539 - val_cls_loss: 0.2358 - val_box_loss: 0.0013 - val_model_loss: 0.2990\n",
            "Epoch 27/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1201 - cls_loss: 0.0526 - box_loss: 2.5220e-04 - model_loss: 0.0652 - val_total_loss: 0.3367 - val_cls_loss: 0.2183 - val_box_loss: 0.0013 - val_model_loss: 0.2819\n",
            "Epoch 28/100\n",
            "58/58 [==============================] - 10s 170ms/step - total_loss: 0.1321 - cls_loss: 0.0596 - box_loss: 3.5220e-04 - model_loss: 0.0772 - val_total_loss: 0.3546 - val_cls_loss: 0.2411 - val_box_loss: 0.0012 - val_model_loss: 0.2998\n",
            "Epoch 29/100\n",
            "58/58 [==============================] - 11s 180ms/step - total_loss: 0.1235 - cls_loss: 0.0536 - box_loss: 3.0165e-04 - model_loss: 0.0687 - val_total_loss: 0.3337 - val_cls_loss: 0.2186 - val_box_loss: 0.0012 - val_model_loss: 0.2788\n",
            "Epoch 30/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.1185 - cls_loss: 0.0497 - box_loss: 2.8005e-04 - model_loss: 0.0637 - val_total_loss: 0.3595 - val_cls_loss: 0.2388 - val_box_loss: 0.0013 - val_model_loss: 0.3047\n",
            "Epoch 31/100\n",
            "58/58 [==============================] - 10s 166ms/step - total_loss: 0.1141 - cls_loss: 0.0478 - box_loss: 2.2938e-04 - model_loss: 0.0593 - val_total_loss: 0.3671 - val_cls_loss: 0.2495 - val_box_loss: 0.0013 - val_model_loss: 0.3123\n",
            "Epoch 32/100\n",
            "58/58 [==============================] - 10s 161ms/step - total_loss: 0.1108 - cls_loss: 0.0441 - box_loss: 2.3839e-04 - model_loss: 0.0560 - val_total_loss: 0.3715 - val_cls_loss: 0.2531 - val_box_loss: 0.0013 - val_model_loss: 0.3166\n",
            "Epoch 33/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.1192 - cls_loss: 0.0502 - box_loss: 2.8361e-04 - model_loss: 0.0644 - val_total_loss: 0.3840 - val_cls_loss: 0.2656 - val_box_loss: 0.0013 - val_model_loss: 0.3292\n",
            "Epoch 34/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1068 - cls_loss: 0.0404 - box_loss: 2.3252e-04 - model_loss: 0.0520 - val_total_loss: 0.3632 - val_cls_loss: 0.2448 - val_box_loss: 0.0013 - val_model_loss: 0.3084\n",
            "Epoch 35/100\n",
            "58/58 [==============================] - 10s 173ms/step - total_loss: 0.1090 - cls_loss: 0.0422 - box_loss: 2.3928e-04 - model_loss: 0.0542 - val_total_loss: 0.3537 - val_cls_loss: 0.2356 - val_box_loss: 0.0013 - val_model_loss: 0.2989\n",
            "Epoch 36/100\n",
            "58/58 [==============================] - 10s 179ms/step - total_loss: 0.0979 - cls_loss: 0.0339 - box_loss: 1.8403e-04 - model_loss: 0.0431 - val_total_loss: 0.3536 - val_cls_loss: 0.2343 - val_box_loss: 0.0013 - val_model_loss: 0.2988\n",
            "Epoch 37/100\n",
            "58/58 [==============================] - 10s 167ms/step - total_loss: 0.1079 - cls_loss: 0.0413 - box_loss: 2.3589e-04 - model_loss: 0.0531 - val_total_loss: 0.3650 - val_cls_loss: 0.2485 - val_box_loss: 0.0012 - val_model_loss: 0.3102\n",
            "Epoch 38/100\n",
            "58/58 [==============================] - 10s 179ms/step - total_loss: 0.1102 - cls_loss: 0.0425 - box_loss: 2.5795e-04 - model_loss: 0.0554 - val_total_loss: 0.3840 - val_cls_loss: 0.2676 - val_box_loss: 0.0012 - val_model_loss: 0.3292\n",
            "Epoch 39/100\n",
            "58/58 [==============================] - 10s 179ms/step - total_loss: 0.1170 - cls_loss: 0.0485 - box_loss: 2.7531e-04 - model_loss: 0.0622 - val_total_loss: 0.4279 - val_cls_loss: 0.3101 - val_box_loss: 0.0013 - val_model_loss: 0.3731\n",
            "Epoch 40/100\n",
            "58/58 [==============================] - 10s 172ms/step - total_loss: 0.1080 - cls_loss: 0.0407 - box_loss: 2.5054e-04 - model_loss: 0.0532 - val_total_loss: 0.3845 - val_cls_loss: 0.2709 - val_box_loss: 0.0012 - val_model_loss: 0.3297\n",
            "Epoch 41/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0958 - cls_loss: 0.0318 - box_loss: 1.8255e-04 - model_loss: 0.0410 - val_total_loss: 0.3940 - val_cls_loss: 0.2801 - val_box_loss: 0.0012 - val_model_loss: 0.3392\n",
            "Epoch 42/100\n",
            "58/58 [==============================] - 10s 171ms/step - total_loss: 0.1120 - cls_loss: 0.0452 - box_loss: 2.3984e-04 - model_loss: 0.0572 - val_total_loss: 0.3940 - val_cls_loss: 0.2783 - val_box_loss: 0.0012 - val_model_loss: 0.3392\n",
            "Epoch 43/100\n",
            "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0990 - cls_loss: 0.0335 - box_loss: 2.1403e-04 - model_loss: 0.0442 - val_total_loss: 0.3919 - val_cls_loss: 0.2798 - val_box_loss: 0.0011 - val_model_loss: 0.3371\n",
            "Epoch 44/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0924 - cls_loss: 0.0290 - box_loss: 1.7189e-04 - model_loss: 0.0376 - val_total_loss: 0.3776 - val_cls_loss: 0.2663 - val_box_loss: 0.0011 - val_model_loss: 0.3228\n",
            "Epoch 45/100\n",
            "58/58 [==============================] - 10s 164ms/step - total_loss: 0.0986 - cls_loss: 0.0338 - box_loss: 2.0052e-04 - model_loss: 0.0438 - val_total_loss: 0.3714 - val_cls_loss: 0.2597 - val_box_loss: 0.0011 - val_model_loss: 0.3166\n",
            "Epoch 46/100\n",
            "58/58 [==============================] - 10s 165ms/step - total_loss: 0.0973 - cls_loss: 0.0318 - box_loss: 2.1465e-04 - model_loss: 0.0426 - val_total_loss: 0.4166 - val_cls_loss: 0.3036 - val_box_loss: 0.0012 - val_model_loss: 0.3618\n",
            "Epoch 47/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0911 - cls_loss: 0.0276 - box_loss: 1.7565e-04 - model_loss: 0.0364 - val_total_loss: 0.4060 - val_cls_loss: 0.2948 - val_box_loss: 0.0011 - val_model_loss: 0.3513\n",
            "Epoch 48/100\n",
            "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0904 - cls_loss: 0.0267 - box_loss: 1.7916e-04 - model_loss: 0.0357 - val_total_loss: 0.3567 - val_cls_loss: 0.2423 - val_box_loss: 0.0012 - val_model_loss: 0.3019\n",
            "Epoch 49/100\n",
            "58/58 [==============================] - 10s 167ms/step - total_loss: 0.0914 - cls_loss: 0.0275 - box_loss: 1.8115e-04 - model_loss: 0.0366 - val_total_loss: 0.3682 - val_cls_loss: 0.2583 - val_box_loss: 0.0011 - val_model_loss: 0.3134\n",
            "Epoch 50/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0884 - cls_loss: 0.0255 - box_loss: 1.6281e-04 - model_loss: 0.0336 - val_total_loss: 0.3971 - val_cls_loss: 0.2865 - val_box_loss: 0.0011 - val_model_loss: 0.3423\n",
            "Epoch 51/100\n",
            "58/58 [==============================] - 10s 168ms/step - total_loss: 0.0885 - cls_loss: 0.0251 - box_loss: 1.7322e-04 - model_loss: 0.0338 - val_total_loss: 0.3973 - val_cls_loss: 0.2851 - val_box_loss: 0.0012 - val_model_loss: 0.3426\n",
            "Epoch 52/100\n",
            "58/58 [==============================] - 10s 166ms/step - total_loss: 0.0823 - cls_loss: 0.0206 - box_loss: 1.4011e-04 - model_loss: 0.0276 - val_total_loss: 0.4406 - val_cls_loss: 0.3295 - val_box_loss: 0.0011 - val_model_loss: 0.3859\n",
            "Epoch 53/100\n",
            "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0823 - cls_loss: 0.0208 - box_loss: 1.3599e-04 - model_loss: 0.0276 - val_total_loss: 0.4206 - val_cls_loss: 0.3097 - val_box_loss: 0.0011 - val_model_loss: 0.3659\n",
            "Epoch 54/100\n",
            "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0920 - cls_loss: 0.0286 - box_loss: 1.7394e-04 - model_loss: 0.0373 - val_total_loss: 0.4128 - val_cls_loss: 0.2998 - val_box_loss: 0.0012 - val_model_loss: 0.3581\n",
            "Epoch 55/100\n",
            "58/58 [==============================] - 9s 160ms/step - total_loss: 0.0919 - cls_loss: 0.0275 - box_loss: 1.9294e-04 - model_loss: 0.0372 - val_total_loss: 0.4181 - val_cls_loss: 0.3057 - val_box_loss: 0.0012 - val_model_loss: 0.3634\n",
            "Epoch 56/100\n",
            "58/58 [==============================] - 10s 170ms/step - total_loss: 0.0902 - cls_loss: 0.0263 - box_loss: 1.8331e-04 - model_loss: 0.0355 - val_total_loss: 0.4495 - val_cls_loss: 0.3346 - val_box_loss: 0.0012 - val_model_loss: 0.3947\n",
            "Epoch 57/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0830 - cls_loss: 0.0208 - box_loss: 1.5004e-04 - model_loss: 0.0283 - val_total_loss: 0.4688 - val_cls_loss: 0.3553 - val_box_loss: 0.0012 - val_model_loss: 0.4141\n",
            "Epoch 58/100\n",
            "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0844 - cls_loss: 0.0213 - box_loss: 1.6770e-04 - model_loss: 0.0296 - val_total_loss: 0.4206 - val_cls_loss: 0.3064 - val_box_loss: 0.0012 - val_model_loss: 0.3659\n",
            "Epoch 59/100\n",
            "58/58 [==============================] - 10s 165ms/step - total_loss: 0.0810 - cls_loss: 0.0194 - box_loss: 1.3744e-04 - model_loss: 0.0263 - val_total_loss: 0.4386 - val_cls_loss: 0.3259 - val_box_loss: 0.0012 - val_model_loss: 0.3839\n",
            "Epoch 60/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0951 - cls_loss: 0.0306 - box_loss: 1.9614e-04 - model_loss: 0.0404 - val_total_loss: 0.4522 - val_cls_loss: 0.3364 - val_box_loss: 0.0012 - val_model_loss: 0.3975\n",
            "Epoch 61/100\n",
            "58/58 [==============================] - 10s 172ms/step - total_loss: 0.0954 - cls_loss: 0.0300 - box_loss: 2.1344e-04 - model_loss: 0.0407 - val_total_loss: 0.4065 - val_cls_loss: 0.2938 - val_box_loss: 0.0012 - val_model_loss: 0.3518\n",
            "Epoch 62/100\n",
            "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0871 - cls_loss: 0.0240 - box_loss: 1.6807e-04 - model_loss: 0.0324 - val_total_loss: 0.3872 - val_cls_loss: 0.2751 - val_box_loss: 0.0011 - val_model_loss: 0.3325\n",
            "Epoch 63/100\n",
            "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0879 - cls_loss: 0.0243 - box_loss: 1.7826e-04 - model_loss: 0.0332 - val_total_loss: 0.4237 - val_cls_loss: 0.3096 - val_box_loss: 0.0012 - val_model_loss: 0.3690\n",
            "Epoch 64/100\n",
            "58/58 [==============================] - 11s 182ms/step - total_loss: 0.0890 - cls_loss: 0.0248 - box_loss: 1.9046e-04 - model_loss: 0.0343 - val_total_loss: 0.4644 - val_cls_loss: 0.3517 - val_box_loss: 0.0012 - val_model_loss: 0.4098\n",
            "Epoch 65/100\n",
            "58/58 [==============================] - 10s 168ms/step - total_loss: 0.0822 - cls_loss: 0.0205 - box_loss: 1.4229e-04 - model_loss: 0.0276 - val_total_loss: 0.4514 - val_cls_loss: 0.3372 - val_box_loss: 0.0012 - val_model_loss: 0.3967\n",
            "Epoch 66/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0771 - cls_loss: 0.0159 - box_loss: 1.3080e-04 - model_loss: 0.0225 - val_total_loss: 0.4708 - val_cls_loss: 0.3569 - val_box_loss: 0.0012 - val_model_loss: 0.4161\n",
            "Epoch 67/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0865 - cls_loss: 0.0234 - box_loss: 1.6953e-04 - model_loss: 0.0318 - val_total_loss: 0.4741 - val_cls_loss: 0.3623 - val_box_loss: 0.0011 - val_model_loss: 0.4194\n",
            "Epoch 68/100\n",
            "58/58 [==============================] - 10s 174ms/step - total_loss: 0.0853 - cls_loss: 0.0222 - box_loss: 1.6866e-04 - model_loss: 0.0306 - val_total_loss: 0.4597 - val_cls_loss: 0.3457 - val_box_loss: 0.0012 - val_model_loss: 0.4051\n",
            "Epoch 69/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0781 - cls_loss: 0.0169 - box_loss: 1.3151e-04 - model_loss: 0.0234 - val_total_loss: 0.4184 - val_cls_loss: 0.3038 - val_box_loss: 0.0012 - val_model_loss: 0.3638\n",
            "Epoch 70/100\n",
            "58/58 [==============================] - 9s 155ms/step - total_loss: 0.0753 - cls_loss: 0.0144 - box_loss: 1.2593e-04 - model_loss: 0.0207 - val_total_loss: 0.4311 - val_cls_loss: 0.3157 - val_box_loss: 0.0012 - val_model_loss: 0.3765\n",
            "Epoch 71/100\n",
            "58/58 [==============================] - 10s 171ms/step - total_loss: 0.0759 - cls_loss: 0.0150 - box_loss: 1.2473e-04 - model_loss: 0.0212 - val_total_loss: 0.4392 - val_cls_loss: 0.3247 - val_box_loss: 0.0012 - val_model_loss: 0.3845\n",
            "Epoch 72/100\n",
            "58/58 [==============================] - 10s 180ms/step - total_loss: 0.0723 - cls_loss: 0.0121 - box_loss: 1.1128e-04 - model_loss: 0.0177 - val_total_loss: 0.4354 - val_cls_loss: 0.3218 - val_box_loss: 0.0012 - val_model_loss: 0.3808\n",
            "Epoch 73/100\n",
            "58/58 [==============================] - 10s 173ms/step - total_loss: 0.0748 - cls_loss: 0.0141 - box_loss: 1.2190e-04 - model_loss: 0.0202 - val_total_loss: 0.4666 - val_cls_loss: 0.3523 - val_box_loss: 0.0012 - val_model_loss: 0.4119\n",
            "Epoch 74/100\n",
            "58/58 [==============================] - 9s 155ms/step - total_loss: 0.0854 - cls_loss: 0.0213 - box_loss: 1.8931e-04 - model_loss: 0.0307 - val_total_loss: 0.4744 - val_cls_loss: 0.3593 - val_box_loss: 0.0012 - val_model_loss: 0.4197\n",
            "Epoch 75/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0722 - cls_loss: 0.0119 - box_loss: 1.1367e-04 - model_loss: 0.0176 - val_total_loss: 0.4579 - val_cls_loss: 0.3441 - val_box_loss: 0.0012 - val_model_loss: 0.4033\n",
            "Epoch 76/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0816 - cls_loss: 0.0194 - box_loss: 1.5098e-04 - model_loss: 0.0270 - val_total_loss: 0.4687 - val_cls_loss: 0.3540 - val_box_loss: 0.0012 - val_model_loss: 0.4141\n",
            "Epoch 77/100\n",
            "58/58 [==============================] - 9s 162ms/step - total_loss: 0.0740 - cls_loss: 0.0136 - box_loss: 1.1443e-04 - model_loss: 0.0194 - val_total_loss: 0.4685 - val_cls_loss: 0.3520 - val_box_loss: 0.0012 - val_model_loss: 0.4139\n",
            "Epoch 78/100\n",
            "58/58 [==============================] - 10s 169ms/step - total_loss: 0.0807 - cls_loss: 0.0188 - box_loss: 1.4684e-04 - model_loss: 0.0261 - val_total_loss: 0.4757 - val_cls_loss: 0.3635 - val_box_loss: 0.0012 - val_model_loss: 0.4211\n",
            "Epoch 79/100\n",
            "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0723 - cls_loss: 0.0122 - box_loss: 1.0911e-04 - model_loss: 0.0177 - val_total_loss: 0.4502 - val_cls_loss: 0.3363 - val_box_loss: 0.0012 - val_model_loss: 0.3956\n",
            "Epoch 80/100\n",
            "58/58 [==============================] - 9s 162ms/step - total_loss: 0.0878 - cls_loss: 0.0246 - box_loss: 1.7331e-04 - model_loss: 0.0332 - val_total_loss: 0.4763 - val_cls_loss: 0.3634 - val_box_loss: 0.0012 - val_model_loss: 0.4218\n",
            "Epoch 81/100\n",
            "58/58 [==============================] - 10s 165ms/step - total_loss: 0.0754 - cls_loss: 0.0145 - box_loss: 1.2602e-04 - model_loss: 0.0208 - val_total_loss: 0.4573 - val_cls_loss: 0.3443 - val_box_loss: 0.0012 - val_model_loss: 0.4027\n",
            "Epoch 82/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0851 - cls_loss: 0.0225 - box_loss: 1.6131e-04 - model_loss: 0.0305 - val_total_loss: 0.5093 - val_cls_loss: 0.3963 - val_box_loss: 0.0012 - val_model_loss: 0.4547\n",
            "Epoch 83/100\n",
            "58/58 [==============================] - 10s 179ms/step - total_loss: 0.0828 - cls_loss: 0.0206 - box_loss: 1.5127e-04 - model_loss: 0.0282 - val_total_loss: 0.3922 - val_cls_loss: 0.2761 - val_box_loss: 0.0012 - val_model_loss: 0.3376\n",
            "Epoch 84/100\n",
            "58/58 [==============================] - 9s 155ms/step - total_loss: 0.0770 - cls_loss: 0.0156 - box_loss: 1.3726e-04 - model_loss: 0.0224 - val_total_loss: 0.4139 - val_cls_loss: 0.2999 - val_box_loss: 0.0012 - val_model_loss: 0.3594\n",
            "Epoch 85/100\n",
            "58/58 [==============================] - 10s 171ms/step - total_loss: 0.0891 - cls_loss: 0.0249 - box_loss: 1.9203e-04 - model_loss: 0.0345 - val_total_loss: 0.3845 - val_cls_loss: 0.2690 - val_box_loss: 0.0012 - val_model_loss: 0.3299\n",
            "Epoch 86/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0809 - cls_loss: 0.0187 - box_loss: 1.5295e-04 - model_loss: 0.0263 - val_total_loss: 0.4956 - val_cls_loss: 0.3830 - val_box_loss: 0.0012 - val_model_loss: 0.4410\n",
            "Epoch 87/100\n",
            "58/58 [==============================] - 9s 153ms/step - total_loss: 0.0750 - cls_loss: 0.0139 - box_loss: 1.3147e-04 - model_loss: 0.0205 - val_total_loss: 0.4752 - val_cls_loss: 0.3636 - val_box_loss: 0.0011 - val_model_loss: 0.4206\n",
            "Epoch 88/100\n",
            "58/58 [==============================] - 10s 164ms/step - total_loss: 0.0719 - cls_loss: 0.0116 - box_loss: 1.1507e-04 - model_loss: 0.0173 - val_total_loss: 0.4785 - val_cls_loss: 0.3651 - val_box_loss: 0.0012 - val_model_loss: 0.4239\n",
            "Epoch 89/100\n",
            "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0716 - cls_loss: 0.0110 - box_loss: 1.2134e-04 - model_loss: 0.0171 - val_total_loss: 0.4705 - val_cls_loss: 0.3568 - val_box_loss: 0.0012 - val_model_loss: 0.4159\n",
            "Epoch 90/100\n",
            "58/58 [==============================] - 10s 167ms/step - total_loss: 0.0709 - cls_loss: 0.0111 - box_loss: 1.0513e-04 - model_loss: 0.0163 - val_total_loss: 0.4822 - val_cls_loss: 0.3693 - val_box_loss: 0.0012 - val_model_loss: 0.4277\n",
            "Epoch 91/100\n",
            "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0724 - cls_loss: 0.0120 - box_loss: 1.1755e-04 - model_loss: 0.0179 - val_total_loss: 0.4846 - val_cls_loss: 0.3707 - val_box_loss: 0.0012 - val_model_loss: 0.4301\n",
            "Epoch 92/100\n",
            "58/58 [==============================] - 11s 180ms/step - total_loss: 0.0713 - cls_loss: 0.0109 - box_loss: 1.1785e-04 - model_loss: 0.0168 - val_total_loss: 0.4797 - val_cls_loss: 0.3647 - val_box_loss: 0.0012 - val_model_loss: 0.4252\n",
            "Epoch 93/100\n",
            "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0720 - cls_loss: 0.0113 - box_loss: 1.2314e-04 - model_loss: 0.0175 - val_total_loss: 0.5127 - val_cls_loss: 0.3989 - val_box_loss: 0.0012 - val_model_loss: 0.4581\n",
            "Epoch 94/100\n",
            "58/58 [==============================] - 11s 179ms/step - total_loss: 0.0727 - cls_loss: 0.0121 - box_loss: 1.2106e-04 - model_loss: 0.0181 - val_total_loss: 0.4858 - val_cls_loss: 0.3714 - val_box_loss: 0.0012 - val_model_loss: 0.4313\n",
            "Epoch 95/100\n",
            "58/58 [==============================] - 11s 181ms/step - total_loss: 0.0688 - cls_loss: 0.0090 - box_loss: 1.0485e-04 - model_loss: 0.0143 - val_total_loss: 0.5133 - val_cls_loss: 0.3996 - val_box_loss: 0.0012 - val_model_loss: 0.4588\n",
            "Epoch 96/100\n",
            "58/58 [==============================] - 10s 170ms/step - total_loss: 0.0794 - cls_loss: 0.0179 - box_loss: 1.3938e-04 - model_loss: 0.0249 - val_total_loss: 0.5106 - val_cls_loss: 0.3938 - val_box_loss: 0.0012 - val_model_loss: 0.4561\n",
            "Epoch 97/100\n",
            "58/58 [==============================] - 11s 184ms/step - total_loss: 0.0961 - cls_loss: 0.0294 - box_loss: 2.4545e-04 - model_loss: 0.0417 - val_total_loss: 0.3818 - val_cls_loss: 0.2698 - val_box_loss: 0.0012 - val_model_loss: 0.3273\n",
            "Epoch 98/100\n",
            "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0697 - cls_loss: 0.0098 - box_loss: 1.0849e-04 - model_loss: 0.0152 - val_total_loss: 0.4305 - val_cls_loss: 0.3191 - val_box_loss: 0.0011 - val_model_loss: 0.3760\n",
            "Epoch 99/100\n",
            "58/58 [==============================] - 9s 154ms/step - total_loss: 0.0684 - cls_loss: 0.0091 - box_loss: 9.6806e-05 - model_loss: 0.0139 - val_total_loss: 0.4636 - val_cls_loss: 0.3514 - val_box_loss: 0.0012 - val_model_loss: 0.4091\n",
            "Epoch 100/100\n",
            "58/58 [==============================] - 10s 174ms/step - total_loss: 0.0729 - cls_loss: 0.0124 - box_loss: 1.2146e-04 - model_loss: 0.0185 - val_total_loss: 0.4777 - val_cls_loss: 0.3659 - val_box_loss: 0.0011 - val_model_loss: 0.4232\n"
          ]
        }
      ],
      "source": [
        "TransferLearning_model.train_model()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "396bIlTlQkwV",
        "outputId": "b1e0b2b5-c831-454c-8fd6-58072811ede9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "21/21 [==============================] - 2s 39ms/step - total_loss: 0.3769 - cls_loss: 0.2622 - box_loss: 0.0012 - model_loss: 0.3225\n",
            "creating index...\n",
            "index created!\n",
            "creating index...\n",
            "index created!\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.21s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.02s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.774\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.955\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.905\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.778\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.747\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.834\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.834\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = -1.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.834\n",
            "Validation loss: [0.3769497871398926, 0.2621723413467407, 0.0012059812434017658, 0.32247138023376465]\n",
            "Validation metrics: {'AP': 0.773797, 'AP50': 0.9550259, 'AP75': 0.9054219, 'APs': -1.0, 'APm': -1.0, 'APl': 0.77800596, 'ARmax1': 0.7468085, 'ARmax10': 0.83404255, 'ARmax100': 0.83404255, 'ARs': -1.0, 'ARm': -1.0, 'ARl': 0.83404255}\n"
          ]
        }
      ],
      "source": [
        "TransferLearning_model.evaluate_model(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rxGJ2UkcQmVR",
        "outputId": "3f52eba3-8e28-4c26-add5-7218ad04fd9f"
      },
      "outputs": [],
      "source": [
        "TransferLearning_model.export_model()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plotting all loss values using matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\aditi\\AppData\\Local\\Temp\\ipykernel_63980\\90527764.py:223: MatplotlibDeprecationWarning: Auto-removal of overlapping axes is deprecated since 3.6 and will be removed two minor releases later; explicitly call ax.remove() as needed.\n",
            "  plt.subplot(4, 2, 2)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x900 with 7 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "# taken from the training log\n",
        "txt_file = \"\"\"\n",
        "Epoch 1/100\n",
        "58/58 [==============================] - 56s 252ms/step - total_loss: 1.6840 - cls_loss: 1.4160 - box_loss: 0.0043 - model_loss: 1.6291 - val_total_loss: 1.3220 - val_cls_loss: 1.1915 - val_box_loss: 0.0015 - val_model_loss: 1.2671\n",
        "Epoch 2/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 1.3179 - cls_loss: 1.1044 - box_loss: 0.0032 - model_loss: 1.2630 - val_total_loss: 1.0755 - val_cls_loss: 0.9413 - val_box_loss: 0.0016 - val_model_loss: 1.0205\n",
        "Epoch 3/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 1.0288 - cls_loss: 0.8628 - box_loss: 0.0022 - model_loss: 0.9738 - val_total_loss: 0.8156 - val_cls_loss: 0.6887 - val_box_loss: 0.0014 - val_model_loss: 0.7607\n",
        "Epoch 4/100\n",
        "58/58 [==============================] - 10s 164ms/step - total_loss: 0.7748 - cls_loss: 0.6346 - box_loss: 0.0017 - model_loss: 0.7198 - val_total_loss: 0.6834 - val_cls_loss: 0.5618 - val_box_loss: 0.0013 - val_model_loss: 0.6285\n",
        "Epoch 5/100\n",
        "58/58 [==============================] - 10s 174ms/step - total_loss: 0.5706 - cls_loss: 0.4493 - box_loss: 0.0013 - model_loss: 0.5156 - val_total_loss: 0.5903 - val_cls_loss: 0.4719 - val_box_loss: 0.0013 - val_model_loss: 0.5354\n",
        "Epoch 6/100\n",
        "58/58 [==============================] - 10s 172ms/step - total_loss: 0.4468 - cls_loss: 0.3364 - box_loss: 0.0011 - model_loss: 0.3918 - val_total_loss: 0.5029 - val_cls_loss: 0.3826 - val_box_loss: 0.0013 - val_model_loss: 0.4479\n",
        "Epoch 7/100\n",
        "58/58 [==============================] - 9s 152ms/step - total_loss: 0.3687 - cls_loss: 0.2690 - box_loss: 8.9543e-04 - model_loss: 0.3138 - val_total_loss: 0.4401 - val_cls_loss: 0.3230 - val_box_loss: 0.0012 - val_model_loss: 0.3851\n",
        "Epoch 8/100\n",
        "58/58 [==============================] - 10s 166ms/step - total_loss: 0.3224 - cls_loss: 0.2254 - box_loss: 8.4212e-04 - model_loss: 0.2675 - val_total_loss: 0.4191 - val_cls_loss: 0.3006 - val_box_loss: 0.0013 - val_model_loss: 0.3641\n",
        "Epoch 9/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.2824 - cls_loss: 0.1937 - box_loss: 6.7377e-04 - model_loss: 0.2274 - val_total_loss: 0.3825 - val_cls_loss: 0.2630 - val_box_loss: 0.0013 - val_model_loss: 0.3276\n",
        "Epoch 10/100\n",
        "58/58 [==============================] - 10s 173ms/step - total_loss: 0.2482 - cls_loss: 0.1637 - box_loss: 5.9065e-04 - model_loss: 0.1932 - val_total_loss: 0.3733 - val_cls_loss: 0.2519 - val_box_loss: 0.0013 - val_model_loss: 0.3184\n",
        "Epoch 11/100\n",
        "58/58 [==============================] - 9s 153ms/step - total_loss: 0.2254 - cls_loss: 0.1436 - box_loss: 5.3736e-04 - model_loss: 0.1705 - val_total_loss: 0.3483 - val_cls_loss: 0.2275 - val_box_loss: 0.0013 - val_model_loss: 0.2934\n",
        "Epoch 12/100\n",
        "58/58 [==============================] - 11s 179ms/step - total_loss: 0.2134 - cls_loss: 0.1337 - box_loss: 4.9445e-04 - model_loss: 0.1584 - val_total_loss: 0.3503 - val_cls_loss: 0.2308 - val_box_loss: 0.0013 - val_model_loss: 0.2954\n",
        "Epoch 13/100\n",
        "58/58 [==============================] - 10s 170ms/step - total_loss: 0.2008 - cls_loss: 0.1197 - box_loss: 5.2362e-04 - model_loss: 0.1459 - val_total_loss: 0.3455 - val_cls_loss: 0.2242 - val_box_loss: 0.0013 - val_model_loss: 0.2906\n",
        "Epoch 14/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.1877 - cls_loss: 0.1109 - box_loss: 4.3546e-04 - model_loss: 0.1327 - val_total_loss: 0.3372 - val_cls_loss: 0.2169 - val_box_loss: 0.0013 - val_model_loss: 0.2823\n",
        "Epoch 15/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1726 - cls_loss: 0.0985 - box_loss: 3.8342e-04 - model_loss: 0.1177 - val_total_loss: 0.3672 - val_cls_loss: 0.2423 - val_box_loss: 0.0014 - val_model_loss: 0.3123\n",
        "Epoch 16/100\n",
        "58/58 [==============================] - 9s 151ms/step - total_loss: 0.1703 - cls_loss: 0.0962 - box_loss: 3.8260e-04 - model_loss: 0.1154 - val_total_loss: 0.3559 - val_cls_loss: 0.2375 - val_box_loss: 0.0013 - val_model_loss: 0.3010\n",
        "Epoch 17/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1692 - cls_loss: 0.0941 - box_loss: 4.0279e-04 - model_loss: 0.1143 - val_total_loss: 0.3512 - val_cls_loss: 0.2347 - val_box_loss: 0.0012 - val_model_loss: 0.2962\n",
        "Epoch 18/100\n",
        "58/58 [==============================] - 10s 174ms/step - total_loss: 0.1515 - cls_loss: 0.0808 - box_loss: 3.1526e-04 - model_loss: 0.0966 - val_total_loss: 0.3413 - val_cls_loss: 0.2211 - val_box_loss: 0.0013 - val_model_loss: 0.2864\n",
        "Epoch 19/100\n",
        "58/58 [==============================] - 9s 155ms/step - total_loss: 0.1478 - cls_loss: 0.0760 - box_loss: 3.3900e-04 - model_loss: 0.0929 - val_total_loss: 0.3312 - val_cls_loss: 0.2146 - val_box_loss: 0.0012 - val_model_loss: 0.2763\n",
        "Epoch 20/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.1476 - cls_loss: 0.0749 - box_loss: 3.5538e-04 - model_loss: 0.0927 - val_total_loss: 0.3337 - val_cls_loss: 0.2179 - val_box_loss: 0.0012 - val_model_loss: 0.2788\n",
        "Epoch 21/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.1448 - cls_loss: 0.0719 - box_loss: 3.5935e-04 - model_loss: 0.0899 - val_total_loss: 0.3377 - val_cls_loss: 0.2228 - val_box_loss: 0.0012 - val_model_loss: 0.2828\n",
        "Epoch 22/100\n",
        "58/58 [==============================] - 9s 155ms/step - total_loss: 0.1327 - cls_loss: 0.0636 - box_loss: 2.8470e-04 - model_loss: 0.0778 - val_total_loss: 0.3492 - val_cls_loss: 0.2320 - val_box_loss: 0.0012 - val_model_loss: 0.2943\n",
        "Epoch 23/100\n",
        "58/58 [==============================] - 10s 167ms/step - total_loss: 0.1315 - cls_loss: 0.0617 - box_loss: 2.9893e-04 - model_loss: 0.0766 - val_total_loss: 0.3484 - val_cls_loss: 0.2308 - val_box_loss: 0.0013 - val_model_loss: 0.2935\n",
        "Epoch 24/100\n",
        "58/58 [==============================] - 11s 182ms/step - total_loss: 0.1334 - cls_loss: 0.0627 - box_loss: 3.1571e-04 - model_loss: 0.0785 - val_total_loss: 0.3256 - val_cls_loss: 0.2088 - val_box_loss: 0.0012 - val_model_loss: 0.2707\n",
        "Epoch 25/100\n",
        "58/58 [==============================] - 11s 179ms/step - total_loss: 0.1261 - cls_loss: 0.0573 - box_loss: 2.7877e-04 - model_loss: 0.0712 - val_total_loss: 0.3409 - val_cls_loss: 0.2243 - val_box_loss: 0.0012 - val_model_loss: 0.2860\n",
        "Epoch 26/100\n",
        "58/58 [==============================] - 10s 173ms/step - total_loss: 0.1251 - cls_loss: 0.0553 - box_loss: 2.9841e-04 - model_loss: 0.0702 - val_total_loss: 0.3539 - val_cls_loss: 0.2358 - val_box_loss: 0.0013 - val_model_loss: 0.2990\n",
        "Epoch 27/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1201 - cls_loss: 0.0526 - box_loss: 2.5220e-04 - model_loss: 0.0652 - val_total_loss: 0.3367 - val_cls_loss: 0.2183 - val_box_loss: 0.0013 - val_model_loss: 0.2819\n",
        "Epoch 28/100\n",
        "58/58 [==============================] - 10s 170ms/step - total_loss: 0.1321 - cls_loss: 0.0596 - box_loss: 3.5220e-04 - model_loss: 0.0772 - val_total_loss: 0.3546 - val_cls_loss: 0.2411 - val_box_loss: 0.0012 - val_model_loss: 0.2998\n",
        "Epoch 29/100\n",
        "58/58 [==============================] - 11s 180ms/step - total_loss: 0.1235 - cls_loss: 0.0536 - box_loss: 3.0165e-04 - model_loss: 0.0687 - val_total_loss: 0.3337 - val_cls_loss: 0.2186 - val_box_loss: 0.0012 - val_model_loss: 0.2788\n",
        "Epoch 30/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.1185 - cls_loss: 0.0497 - box_loss: 2.8005e-04 - model_loss: 0.0637 - val_total_loss: 0.3595 - val_cls_loss: 0.2388 - val_box_loss: 0.0013 - val_model_loss: 0.3047\n",
        "Epoch 31/100\n",
        "58/58 [==============================] - 10s 166ms/step - total_loss: 0.1141 - cls_loss: 0.0478 - box_loss: 2.2938e-04 - model_loss: 0.0593 - val_total_loss: 0.3671 - val_cls_loss: 0.2495 - val_box_loss: 0.0013 - val_model_loss: 0.3123\n",
        "Epoch 32/100\n",
        "58/58 [==============================] - 10s 161ms/step - total_loss: 0.1108 - cls_loss: 0.0441 - box_loss: 2.3839e-04 - model_loss: 0.0560 - val_total_loss: 0.3715 - val_cls_loss: 0.2531 - val_box_loss: 0.0013 - val_model_loss: 0.3166\n",
        "Epoch 33/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.1192 - cls_loss: 0.0502 - box_loss: 2.8361e-04 - model_loss: 0.0644 - val_total_loss: 0.3840 - val_cls_loss: 0.2656 - val_box_loss: 0.0013 - val_model_loss: 0.3292\n",
        "Epoch 34/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.1068 - cls_loss: 0.0404 - box_loss: 2.3252e-04 - model_loss: 0.0520 - val_total_loss: 0.3632 - val_cls_loss: 0.2448 - val_box_loss: 0.0013 - val_model_loss: 0.3084\n",
        "Epoch 35/100\n",
        "58/58 [==============================] - 10s 173ms/step - total_loss: 0.1090 - cls_loss: 0.0422 - box_loss: 2.3928e-04 - model_loss: 0.0542 - val_total_loss: 0.3537 - val_cls_loss: 0.2356 - val_box_loss: 0.0013 - val_model_loss: 0.2989\n",
        "Epoch 36/100\n",
        "58/58 [==============================] - 10s 179ms/step - total_loss: 0.0979 - cls_loss: 0.0339 - box_loss: 1.8403e-04 - model_loss: 0.0431 - val_total_loss: 0.3536 - val_cls_loss: 0.2343 - val_box_loss: 0.0013 - val_model_loss: 0.2988\n",
        "Epoch 37/100\n",
        "58/58 [==============================] - 10s 167ms/step - total_loss: 0.1079 - cls_loss: 0.0413 - box_loss: 2.3589e-04 - model_loss: 0.0531 - val_total_loss: 0.3650 - val_cls_loss: 0.2485 - val_box_loss: 0.0012 - val_model_loss: 0.3102\n",
        "Epoch 38/100\n",
        "58/58 [==============================] - 10s 179ms/step - total_loss: 0.1102 - cls_loss: 0.0425 - box_loss: 2.5795e-04 - model_loss: 0.0554 - val_total_loss: 0.3840 - val_cls_loss: 0.2676 - val_box_loss: 0.0012 - val_model_loss: 0.3292\n",
        "Epoch 39/100\n",
        "58/58 [==============================] - 10s 179ms/step - total_loss: 0.1170 - cls_loss: 0.0485 - box_loss: 2.7531e-04 - model_loss: 0.0622 - val_total_loss: 0.4279 - val_cls_loss: 0.3101 - val_box_loss: 0.0013 - val_model_loss: 0.3731\n",
        "Epoch 40/100\n",
        "58/58 [==============================] - 10s 172ms/step - total_loss: 0.1080 - cls_loss: 0.0407 - box_loss: 2.5054e-04 - model_loss: 0.0532 - val_total_loss: 0.3845 - val_cls_loss: 0.2709 - val_box_loss: 0.0012 - val_model_loss: 0.3297\n",
        "Epoch 41/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0958 - cls_loss: 0.0318 - box_loss: 1.8255e-04 - model_loss: 0.0410 - val_total_loss: 0.3940 - val_cls_loss: 0.2801 - val_box_loss: 0.0012 - val_model_loss: 0.3392\n",
        "Epoch 42/100\n",
        "58/58 [==============================] - 10s 171ms/step - total_loss: 0.1120 - cls_loss: 0.0452 - box_loss: 2.3984e-04 - model_loss: 0.0572 - val_total_loss: 0.3940 - val_cls_loss: 0.2783 - val_box_loss: 0.0012 - val_model_loss: 0.3392\n",
        "Epoch 43/100\n",
        "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0990 - cls_loss: 0.0335 - box_loss: 2.1403e-04 - model_loss: 0.0442 - val_total_loss: 0.3919 - val_cls_loss: 0.2798 - val_box_loss: 0.0011 - val_model_loss: 0.3371\n",
        "Epoch 44/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0924 - cls_loss: 0.0290 - box_loss: 1.7189e-04 - model_loss: 0.0376 - val_total_loss: 0.3776 - val_cls_loss: 0.2663 - val_box_loss: 0.0011 - val_model_loss: 0.3228\n",
        "Epoch 45/100\n",
        "58/58 [==============================] - 10s 164ms/step - total_loss: 0.0986 - cls_loss: 0.0338 - box_loss: 2.0052e-04 - model_loss: 0.0438 - val_total_loss: 0.3714 - val_cls_loss: 0.2597 - val_box_loss: 0.0011 - val_model_loss: 0.3166\n",
        "Epoch 46/100\n",
        "58/58 [==============================] - 10s 165ms/step - total_loss: 0.0973 - cls_loss: 0.0318 - box_loss: 2.1465e-04 - model_loss: 0.0426 - val_total_loss: 0.4166 - val_cls_loss: 0.3036 - val_box_loss: 0.0012 - val_model_loss: 0.3618\n",
        "Epoch 47/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0911 - cls_loss: 0.0276 - box_loss: 1.7565e-04 - model_loss: 0.0364 - val_total_loss: 0.4060 - val_cls_loss: 0.2948 - val_box_loss: 0.0011 - val_model_loss: 0.3513\n",
        "Epoch 48/100\n",
        "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0904 - cls_loss: 0.0267 - box_loss: 1.7916e-04 - model_loss: 0.0357 - val_total_loss: 0.3567 - val_cls_loss: 0.2423 - val_box_loss: 0.0012 - val_model_loss: 0.3019\n",
        "Epoch 49/100\n",
        "58/58 [==============================] - 10s 167ms/step - total_loss: 0.0914 - cls_loss: 0.0275 - box_loss: 1.8115e-04 - model_loss: 0.0366 - val_total_loss: 0.3682 - val_cls_loss: 0.2583 - val_box_loss: 0.0011 - val_model_loss: 0.3134\n",
        "Epoch 50/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0884 - cls_loss: 0.0255 - box_loss: 1.6281e-04 - model_loss: 0.0336 - val_total_loss: 0.3971 - val_cls_loss: 0.2865 - val_box_loss: 0.0011 - val_model_loss: 0.3423\n",
        "Epoch 51/100\n",
        "58/58 [==============================] - 10s 168ms/step - total_loss: 0.0885 - cls_loss: 0.0251 - box_loss: 1.7322e-04 - model_loss: 0.0338 - val_total_loss: 0.3973 - val_cls_loss: 0.2851 - val_box_loss: 0.0012 - val_model_loss: 0.3426\n",
        "Epoch 52/100\n",
        "58/58 [==============================] - 10s 166ms/step - total_loss: 0.0823 - cls_loss: 0.0206 - box_loss: 1.4011e-04 - model_loss: 0.0276 - val_total_loss: 0.4406 - val_cls_loss: 0.3295 - val_box_loss: 0.0011 - val_model_loss: 0.3859\n",
        "Epoch 53/100\n",
        "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0823 - cls_loss: 0.0208 - box_loss: 1.3599e-04 - model_loss: 0.0276 - val_total_loss: 0.4206 - val_cls_loss: 0.3097 - val_box_loss: 0.0011 - val_model_loss: 0.3659\n",
        "Epoch 54/100\n",
        "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0920 - cls_loss: 0.0286 - box_loss: 1.7394e-04 - model_loss: 0.0373 - val_total_loss: 0.4128 - val_cls_loss: 0.2998 - val_box_loss: 0.0012 - val_model_loss: 0.3581\n",
        "Epoch 55/100\n",
        "58/58 [==============================] - 9s 160ms/step - total_loss: 0.0919 - cls_loss: 0.0275 - box_loss: 1.9294e-04 - model_loss: 0.0372 - val_total_loss: 0.4181 - val_cls_loss: 0.3057 - val_box_loss: 0.0012 - val_model_loss: 0.3634\n",
        "Epoch 56/100\n",
        "58/58 [==============================] - 10s 170ms/step - total_loss: 0.0902 - cls_loss: 0.0263 - box_loss: 1.8331e-04 - model_loss: 0.0355 - val_total_loss: 0.4495 - val_cls_loss: 0.3346 - val_box_loss: 0.0012 - val_model_loss: 0.3947\n",
        "Epoch 57/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0830 - cls_loss: 0.0208 - box_loss: 1.5004e-04 - model_loss: 0.0283 - val_total_loss: 0.4688 - val_cls_loss: 0.3553 - val_box_loss: 0.0012 - val_model_loss: 0.4141\n",
        "Epoch 58/100\n",
        "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0844 - cls_loss: 0.0213 - box_loss: 1.6770e-04 - model_loss: 0.0296 - val_total_loss: 0.4206 - val_cls_loss: 0.3064 - val_box_loss: 0.0012 - val_model_loss: 0.3659\n",
        "Epoch 59/100\n",
        "58/58 [==============================] - 10s 165ms/step - total_loss: 0.0810 - cls_loss: 0.0194 - box_loss: 1.3744e-04 - model_loss: 0.0263 - val_total_loss: 0.4386 - val_cls_loss: 0.3259 - val_box_loss: 0.0012 - val_model_loss: 0.3839\n",
        "Epoch 60/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0951 - cls_loss: 0.0306 - box_loss: 1.9614e-04 - model_loss: 0.0404 - val_total_loss: 0.4522 - val_cls_loss: 0.3364 - val_box_loss: 0.0012 - val_model_loss: 0.3975\n",
        "Epoch 61/100\n",
        "58/58 [==============================] - 10s 172ms/step - total_loss: 0.0954 - cls_loss: 0.0300 - box_loss: 2.1344e-04 - model_loss: 0.0407 - val_total_loss: 0.4065 - val_cls_loss: 0.2938 - val_box_loss: 0.0012 - val_model_loss: 0.3518\n",
        "Epoch 62/100\n",
        "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0871 - cls_loss: 0.0240 - box_loss: 1.6807e-04 - model_loss: 0.0324 - val_total_loss: 0.3872 - val_cls_loss: 0.2751 - val_box_loss: 0.0011 - val_model_loss: 0.3325\n",
        "Epoch 63/100\n",
        "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0879 - cls_loss: 0.0243 - box_loss: 1.7826e-04 - model_loss: 0.0332 - val_total_loss: 0.4237 - val_cls_loss: 0.3096 - val_box_loss: 0.0012 - val_model_loss: 0.3690\n",
        "Epoch 64/100\n",
        "58/58 [==============================] - 11s 182ms/step - total_loss: 0.0890 - cls_loss: 0.0248 - box_loss: 1.9046e-04 - model_loss: 0.0343 - val_total_loss: 0.4644 - val_cls_loss: 0.3517 - val_box_loss: 0.0012 - val_model_loss: 0.4098\n",
        "Epoch 65/100\n",
        "58/58 [==============================] - 10s 168ms/step - total_loss: 0.0822 - cls_loss: 0.0205 - box_loss: 1.4229e-04 - model_loss: 0.0276 - val_total_loss: 0.4514 - val_cls_loss: 0.3372 - val_box_loss: 0.0012 - val_model_loss: 0.3967\n",
        "Epoch 66/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0771 - cls_loss: 0.0159 - box_loss: 1.3080e-04 - model_loss: 0.0225 - val_total_loss: 0.4708 - val_cls_loss: 0.3569 - val_box_loss: 0.0012 - val_model_loss: 0.4161\n",
        "Epoch 67/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0865 - cls_loss: 0.0234 - box_loss: 1.6953e-04 - model_loss: 0.0318 - val_total_loss: 0.4741 - val_cls_loss: 0.3623 - val_box_loss: 0.0011 - val_model_loss: 0.4194\n",
        "Epoch 68/100\n",
        "58/58 [==============================] - 10s 174ms/step - total_loss: 0.0853 - cls_loss: 0.0222 - box_loss: 1.6866e-04 - model_loss: 0.0306 - val_total_loss: 0.4597 - val_cls_loss: 0.3457 - val_box_loss: 0.0012 - val_model_loss: 0.4051\n",
        "Epoch 69/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0781 - cls_loss: 0.0169 - box_loss: 1.3151e-04 - model_loss: 0.0234 - val_total_loss: 0.4184 - val_cls_loss: 0.3038 - val_box_loss: 0.0012 - val_model_loss: 0.3638\n",
        "Epoch 70/100\n",
        "58/58 [==============================] - 9s 155ms/step - total_loss: 0.0753 - cls_loss: 0.0144 - box_loss: 1.2593e-04 - model_loss: 0.0207 - val_total_loss: 0.4311 - val_cls_loss: 0.3157 - val_box_loss: 0.0012 - val_model_loss: 0.3765\n",
        "Epoch 71/100\n",
        "58/58 [==============================] - 10s 171ms/step - total_loss: 0.0759 - cls_loss: 0.0150 - box_loss: 1.2473e-04 - model_loss: 0.0212 - val_total_loss: 0.4392 - val_cls_loss: 0.3247 - val_box_loss: 0.0012 - val_model_loss: 0.3845\n",
        "Epoch 72/100\n",
        "58/58 [==============================] - 10s 180ms/step - total_loss: 0.0723 - cls_loss: 0.0121 - box_loss: 1.1128e-04 - model_loss: 0.0177 - val_total_loss: 0.4354 - val_cls_loss: 0.3218 - val_box_loss: 0.0012 - val_model_loss: 0.3808\n",
        "Epoch 73/100\n",
        "58/58 [==============================] - 10s 173ms/step - total_loss: 0.0748 - cls_loss: 0.0141 - box_loss: 1.2190e-04 - model_loss: 0.0202 - val_total_loss: 0.4666 - val_cls_loss: 0.3523 - val_box_loss: 0.0012 - val_model_loss: 0.4119\n",
        "Epoch 74/100\n",
        "58/58 [==============================] - 9s 155ms/step - total_loss: 0.0854 - cls_loss: 0.0213 - box_loss: 1.8931e-04 - model_loss: 0.0307 - val_total_loss: 0.4744 - val_cls_loss: 0.3593 - val_box_loss: 0.0012 - val_model_loss: 0.4197\n",
        "Epoch 75/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0722 - cls_loss: 0.0119 - box_loss: 1.1367e-04 - model_loss: 0.0176 - val_total_loss: 0.4579 - val_cls_loss: 0.3441 - val_box_loss: 0.0012 - val_model_loss: 0.4033\n",
        "Epoch 76/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0816 - cls_loss: 0.0194 - box_loss: 1.5098e-04 - model_loss: 0.0270 - val_total_loss: 0.4687 - val_cls_loss: 0.3540 - val_box_loss: 0.0012 - val_model_loss: 0.4141\n",
        "Epoch 77/100\n",
        "58/58 [==============================] - 9s 162ms/step - total_loss: 0.0740 - cls_loss: 0.0136 - box_loss: 1.1443e-04 - model_loss: 0.0194 - val_total_loss: 0.4685 - val_cls_loss: 0.3520 - val_box_loss: 0.0012 - val_model_loss: 0.4139\n",
        "Epoch 78/100\n",
        "58/58 [==============================] - 10s 169ms/step - total_loss: 0.0807 - cls_loss: 0.0188 - box_loss: 1.4684e-04 - model_loss: 0.0261 - val_total_loss: 0.4757 - val_cls_loss: 0.3635 - val_box_loss: 0.0012 - val_model_loss: 0.4211\n",
        "Epoch 79/100\n",
        "58/58 [==============================] - 10s 177ms/step - total_loss: 0.0723 - cls_loss: 0.0122 - box_loss: 1.0911e-04 - model_loss: 0.0177 - val_total_loss: 0.4502 - val_cls_loss: 0.3363 - val_box_loss: 0.0012 - val_model_loss: 0.3956\n",
        "Epoch 80/100\n",
        "58/58 [==============================] - 9s 162ms/step - total_loss: 0.0878 - cls_loss: 0.0246 - box_loss: 1.7331e-04 - model_loss: 0.0332 - val_total_loss: 0.4763 - val_cls_loss: 0.3634 - val_box_loss: 0.0012 - val_model_loss: 0.4218\n",
        "Epoch 81/100\n",
        "58/58 [==============================] - 10s 165ms/step - total_loss: 0.0754 - cls_loss: 0.0145 - box_loss: 1.2602e-04 - model_loss: 0.0208 - val_total_loss: 0.4573 - val_cls_loss: 0.3443 - val_box_loss: 0.0012 - val_model_loss: 0.4027\n",
        "Epoch 82/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0851 - cls_loss: 0.0225 - box_loss: 1.6131e-04 - model_loss: 0.0305 - val_total_loss: 0.5093 - val_cls_loss: 0.3963 - val_box_loss: 0.0012 - val_model_loss: 0.4547\n",
        "Epoch 83/100\n",
        "58/58 [==============================] - 10s 179ms/step - total_loss: 0.0828 - cls_loss: 0.0206 - box_loss: 1.5127e-04 - model_loss: 0.0282 - val_total_loss: 0.3922 - val_cls_loss: 0.2761 - val_box_loss: 0.0012 - val_model_loss: 0.3376\n",
        "Epoch 84/100\n",
        "58/58 [==============================] - 9s 155ms/step - total_loss: 0.0770 - cls_loss: 0.0156 - box_loss: 1.3726e-04 - model_loss: 0.0224 - val_total_loss: 0.4139 - val_cls_loss: 0.2999 - val_box_loss: 0.0012 - val_model_loss: 0.3594\n",
        "Epoch 85/100\n",
        "58/58 [==============================] - 10s 171ms/step - total_loss: 0.0891 - cls_loss: 0.0249 - box_loss: 1.9203e-04 - model_loss: 0.0345 - val_total_loss: 0.3845 - val_cls_loss: 0.2690 - val_box_loss: 0.0012 - val_model_loss: 0.3299\n",
        "Epoch 86/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0809 - cls_loss: 0.0187 - box_loss: 1.5295e-04 - model_loss: 0.0263 - val_total_loss: 0.4956 - val_cls_loss: 0.3830 - val_box_loss: 0.0012 - val_model_loss: 0.4410\n",
        "Epoch 87/100\n",
        "58/58 [==============================] - 9s 153ms/step - total_loss: 0.0750 - cls_loss: 0.0139 - box_loss: 1.3147e-04 - model_loss: 0.0205 - val_total_loss: 0.4752 - val_cls_loss: 0.3636 - val_box_loss: 0.0011 - val_model_loss: 0.4206\n",
        "Epoch 88/100\n",
        "58/58 [==============================] - 10s 164ms/step - total_loss: 0.0719 - cls_loss: 0.0116 - box_loss: 1.1507e-04 - model_loss: 0.0173 - val_total_loss: 0.4785 - val_cls_loss: 0.3651 - val_box_loss: 0.0012 - val_model_loss: 0.4239\n",
        "Epoch 89/100\n",
        "58/58 [==============================] - 10s 176ms/step - total_loss: 0.0716 - cls_loss: 0.0110 - box_loss: 1.2134e-04 - model_loss: 0.0171 - val_total_loss: 0.4705 - val_cls_loss: 0.3568 - val_box_loss: 0.0012 - val_model_loss: 0.4159\n",
        "Epoch 90/100\n",
        "58/58 [==============================] - 10s 167ms/step - total_loss: 0.0709 - cls_loss: 0.0111 - box_loss: 1.0513e-04 - model_loss: 0.0163 - val_total_loss: 0.4822 - val_cls_loss: 0.3693 - val_box_loss: 0.0012 - val_model_loss: 0.4277\n",
        "Epoch 91/100\n",
        "58/58 [==============================] - 10s 163ms/step - total_loss: 0.0724 - cls_loss: 0.0120 - box_loss: 1.1755e-04 - model_loss: 0.0179 - val_total_loss: 0.4846 - val_cls_loss: 0.3707 - val_box_loss: 0.0012 - val_model_loss: 0.4301\n",
        "Epoch 92/100\n",
        "58/58 [==============================] - 11s 180ms/step - total_loss: 0.0713 - cls_loss: 0.0109 - box_loss: 1.1785e-04 - model_loss: 0.0168 - val_total_loss: 0.4797 - val_cls_loss: 0.3647 - val_box_loss: 0.0012 - val_model_loss: 0.4252\n",
        "Epoch 93/100\n",
        "58/58 [==============================] - 10s 175ms/step - total_loss: 0.0720 - cls_loss: 0.0113 - box_loss: 1.2314e-04 - model_loss: 0.0175 - val_total_loss: 0.5127 - val_cls_loss: 0.3989 - val_box_loss: 0.0012 - val_model_loss: 0.4581\n",
        "Epoch 94/100\n",
        "58/58 [==============================] - 11s 179ms/step - total_loss: 0.0727 - cls_loss: 0.0121 - box_loss: 1.2106e-04 - model_loss: 0.0181 - val_total_loss: 0.4858 - val_cls_loss: 0.3714 - val_box_loss: 0.0012 - val_model_loss: 0.4313\n",
        "Epoch 95/100\n",
        "58/58 [==============================] - 11s 181ms/step - total_loss: 0.0688 - cls_loss: 0.0090 - box_loss: 1.0485e-04 - model_loss: 0.0143 - val_total_loss: 0.5133 - val_cls_loss: 0.3996 - val_box_loss: 0.0012 - val_model_loss: 0.4588\n",
        "Epoch 96/100\n",
        "58/58 [==============================] - 10s 170ms/step - total_loss: 0.0794 - cls_loss: 0.0179 - box_loss: 1.3938e-04 - model_loss: 0.0249 - val_total_loss: 0.5106 - val_cls_loss: 0.3938 - val_box_loss: 0.0012 - val_model_loss: 0.4561\n",
        "Epoch 97/100\n",
        "58/58 [==============================] - 11s 184ms/step - total_loss: 0.0961 - cls_loss: 0.0294 - box_loss: 2.4545e-04 - model_loss: 0.0417 - val_total_loss: 0.3818 - val_cls_loss: 0.2698 - val_box_loss: 0.0012 - val_model_loss: 0.3273\n",
        "Epoch 98/100\n",
        "58/58 [==============================] - 10s 178ms/step - total_loss: 0.0697 - cls_loss: 0.0098 - box_loss: 1.0849e-04 - model_loss: 0.0152 - val_total_loss: 0.4305 - val_cls_loss: 0.3191 - val_box_loss: 0.0011 - val_model_loss: 0.3760\n",
        "Epoch 99/100\n",
        "58/58 [==============================] - 9s 154ms/step - total_loss: 0.0684 - cls_loss: 0.0091 - box_loss: 9.6806e-05 - model_loss: 0.0139 - val_total_loss: 0.4636 - val_cls_loss: 0.3514 - val_box_loss: 0.0012 - val_model_loss: 0.4091\n",
        "Epoch 100/100\n",
        "58/58 [==============================] - 10s 174ms/step - total_loss: 0.0729 - cls_loss: 0.0124 - box_loss: 1.2146e-04 - model_loss: 0.0185 - val_total_loss: 0.4777 - val_cls_loss: 0.3659 - val_box_loss: 0.0011 - val_model_loss: 0.4232\n",
        "\"\"\"\n",
        "pattern = r\"total_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - cls_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - box_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - model_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_total_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_cls_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_box_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?) - val_model_loss: (\\d+\\.\\d+(?:e[+-]?\\d+)?)\"\n",
        "\n",
        "matches = re.findall(pattern, txt_file)\n",
        "# Convert the matched strings to float and separate them into lists\n",
        "total_losses, cls_losses, box_losses, model_losses, val_total_losses, val_cls_losses, val_box_losses, val_model_losses = zip(*((float(total), float(cls), float(box), float(model), float(val_total), float(val_cls), float(val_box), float(val_model)) \n",
        "                                                           for total, cls, box, model,val_total, val_cls, val_box, val_model in matches))\n",
        "\n",
        "epochs = range(1, len(total_losses) + 1)\n",
        "\n",
        "plt.figure(figsize=(10,9))\n",
        "plt.plot(epochs, total_losses, ('#DB2E4B'), label=\"Total Loss\", linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Total Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.subplot(4, 2, 2)\n",
        "plt.plot(epochs, cls_losses, ('#DB2E4B'), label=\"Classification Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Class Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.subplot(4, 2, 3)\n",
        "plt.plot(epochs, box_losses, ('#DB2E4B'), label=\"Box Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Box Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.subplot(4, 2,4)\n",
        "plt.plot(epochs, model_losses, ('#DB2E4B'), label=\"Model Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Model Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "\n",
        "plt.subplot(4, 2, 5)\n",
        "plt.plot(epochs, val_total_losses, ('#DB2E4B'), label=\"Validation Total Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Validation Total Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.subplot(4, 2, 6)\n",
        "plt.plot(epochs, val_cls_losses, ('#DB2E4B'), label=\"Validation Classification Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Validation Classification Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.subplot(4, 2, 7)\n",
        "plt.plot(epochs, val_box_losses, ('#DB2E4B'), label=\"Validation Box Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Validation Box Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.subplot(4, 2, 8)\n",
        "plt.plot(epochs, val_model_losses, ('#DB2E4B'), label=\"Validation Model Loss\",linewidth=4)\n",
        "plt.xlabel(\"Epochs\",fontsize=16)\n",
        "plt.ylabel(\"Validation Model Loss\",fontsize=16)\n",
        "plt.legend(fontsize=16)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plotting average precision, average recall based on the IoU threshold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1600x1300 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "#Given data\n",
        "metrics = {\n",
        "    'AP': [0.774, 0.955, 0.905, 1.000,1.000, 0.778],\n",
        "    'AR': [0.747, 0.834, 0.834,1.000, 1.000, 0.834]\n",
        "}\n",
        "categories = ['0.50:0.95 all', '0.50 all', '0.75 all',\n",
        "              '0.50:0.95 small', '0.50:0.95 med', '0.50:0.95 large']\n",
        "# Create a bar plot for AP and AR with bars side by side\n",
        "bar_width = 0.35  # the width of the bars\n",
        "index = np.arange(len(metrics['AP']))  # the label locations\n",
        "\n",
        "fig, ax1 = plt.subplots(figsize=(16,13))\n",
        "\n",
        "# Bar plots\n",
        "rects1 = ax1.bar(index - bar_width/2, metrics['AP'], bar_width, label='Average Precision (AP)', color='#DB2E4B') # noqa\n",
        "rects2 = ax1.bar(index + bar_width/2, metrics['AR'], bar_width, label='Average Recall (AR)', color='#FFF572') # noqa\n",
        "\n",
        "\n",
        "# Labels and legends\n",
        "ax1.set_xlabel('IoU Thresholds and Categories',fontsize=16)\n",
        "ax1.set_ylabel(' Metric value',fontsize=16)\n",
        "ax1.set_title('Average Precision, Average Recall per Category(People)',fontsize=20)\n",
        "\n",
        "# Adjust tick label size (new)\n",
        "ax1.tick_params(axis='x', labelsize=14)\n",
        "ax1.tick_params(axis='y', labelsize=14)\n",
        "\n",
        "ax1.set_xticks(index)\n",
        "ax1.set_xticklabels(categories, rotation=45, fontsize=12)\n",
        "\n",
        "ax1.legend(loc='upper left',fontsize = '20')\n",
        "plt.savefig('barplot.png')\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "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": "3.8.19"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}