24_object_classification_and_localization.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHpM76v9_gZt",
        "outputId": "67ecf5d5-f276-4a80-b470-383d6aaae26e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'object-detection-notebooks'...\n",
            "remote: Enumerating objects: 2083, done.\u001b[K\n",
            "remote: Counting objects: 100% (2083/2083), done.\u001b[K\n",
            "remote: Compressing objects: 100% (2078/2078), done.\u001b[K\n",
            "remote: Total 2083 (delta 9), reused 2069 (delta 2), pack-reused 0 (from 0)\u001b[K\n",
            "Receiving objects: 100% (2083/2083), 26.78 MiB | 16.73 MiB/s, done.\n",
            "Resolving deltas: 100% (9/9), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/Alireza-Akhavan/object-detection-notebooks.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bp9h_Qwa_gZu"
      },
      "source": [
        "This notebook is part of \"Object detection course\":\n",
        "    https://github.com/Alireza-Akhavan/object-detection-notebooks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "TfVGckI4_gZv"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.applications.vgg16 import VGG16\n",
        "from tensorflow.keras.layers import Flatten\n",
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import Input\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from sklearn.preprocessing import LabelBinarizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pickle\n",
        "import cv2\n",
        "import os\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "jambKVhK_gZv"
      },
      "outputs": [],
      "source": [
        "# define the base path to the input dataset and then use it to derive\n",
        "# the path to the input images and annotation CSV files\n",
        "BASE_PATH = \"/content/object-detection-notebooks/dataset2\"\n",
        "IMAGES_PATH = os.path.sep.join([BASE_PATH, \"images\"])\n",
        "ANNOTS_PATH = os.path.sep.join([BASE_PATH, \"annotations\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZH49MwAr_gZw"
      },
      "source": [
        "initialize the list of data (images), class labels, target bounding box coordinates, and image paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "c-xeLk1R_gZw"
      },
      "outputs": [],
      "source": [
        "data = []\n",
        "labels = []\n",
        "bboxes = []\n",
        "imagePaths = []"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2IBKSS7F_gZw",
        "outputId": "c05eb6ac-8e3c-4675-f285-956c98ad5d99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/object-detection-notebooks/dataset2/annotations/airplane.csv',\n",
              " '/content/object-detection-notebooks/dataset2/annotations/motorcycle.csv',\n",
              " '/content/object-detection-notebooks/dataset2/annotations/face.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "csvPaths = []\n",
        "for (rootDir, dirNames, filenames) in os.walk(ANNOTS_PATH):\n",
        "    # loop over the filenames in the current directory\n",
        "    for filename in filenames:\n",
        "        # determine the file extension of the current file\n",
        "        ext = filename[filename.rfind(\".\"):].lower()\n",
        "\n",
        "        # check to see if the file is an image and should be processed\n",
        "        if ext.endswith(\".csv\"):\n",
        "            # construct the path to the image and yield it\n",
        "            csvPath = os.path.join(rootDir, filename)\n",
        "            csvPaths.append(csvPath)\n",
        "\n",
        "csvPaths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "u7dbNAfF_gZw"
      },
      "outputs": [],
      "source": [
        "# loop over all CSV files in the annotations directory\n",
        "for csvPath in csvPaths:\n",
        "    # load the contents of the current CSV annotations file\n",
        "    rows = open(csvPath).read().strip().split(\"\\n\")\n",
        "\n",
        "    # loop over the rows\n",
        "    for row in rows:\n",
        "        # break the row into the filename, bounding box coordinates,\n",
        "        # and class label\n",
        "        row = row.split(\",\")\n",
        "        (filename, startX, startY, endX, endY, label) = row\n",
        "\n",
        "        # derive the path to the input image, load the image (in\n",
        "        # OpenCV format), and grab its dimensions\n",
        "        imagePath = os.path.sep.join([IMAGES_PATH, label, filename])\n",
        "        image = cv2.imread(imagePath)\n",
        "        (h, w) = image.shape[:2]\n",
        "\n",
        "        # scale the bounding box coordinates relative to the spatial\n",
        "        # dimensions of the input image\n",
        "        startX = float(startX) / w\n",
        "        startY = float(startY) / h\n",
        "        endX = float(endX) / w\n",
        "        endY = float(endY) / h\n",
        "\n",
        "        # load the image and preprocess it\n",
        "        image = load_img(imagePath, target_size=(224, 224))\n",
        "        image = img_to_array(image)\n",
        "\n",
        "        # update our list of data, class labels, bounding boxes, and\n",
        "        # image paths\n",
        "        data.append(image)\n",
        "        labels.append(label)\n",
        "        bboxes.append((startX, startY, endX, endY))\n",
        "        imagePaths.append(imagePath)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "1FL4Q2As_gZx"
      },
      "outputs": [],
      "source": [
        "# convert the data, class labels, bounding boxes, and image paths to\n",
        "# NumPy arrays, scaling the input pixel intensities from the range\n",
        "# [0, 255] to [0, 1]\n",
        "data = np.array(data, dtype=\"float32\") / 255.0\n",
        "labels = np.array(labels)\n",
        "bboxes = np.array(bboxes, dtype=\"float32\")\n",
        "imagePaths = np.array(imagePaths)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "e2HbZllo_gZx"
      },
      "outputs": [],
      "source": [
        "# perform one-hot encoding on the labels\n",
        "lb = LabelBinarizer()\n",
        "labels = lb.fit_transform(labels)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lb.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jSUXsrmfA16k",
        "outputId": "4646d10e-e212-4618-aefc-be0e7eb228b7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['airplane', 'face', 'motorcycle'], dtype='<U10')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gmz-gWB-AzeN",
        "outputId": "0f0becdb-854a-4523-b235-245e9e72fbc8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "455UL1k9_gZx"
      },
      "outputs": [],
      "source": [
        "# partition the data into training and testing splits using 80% of\n",
        "# the data for training and the remaining 20% for testing\n",
        "split = train_test_split(data, labels, bboxes, imagePaths, test_size=0.20, random_state=42)\n",
        "# unpack the data split\n",
        "(trainImages, testImages) = split[:2]\n",
        "(trainLabels, testLabels) = split[2:4]\n",
        "(trainBBoxes, testBBoxes) = split[4:6]\n",
        "(trainPaths, testPaths) = split[6:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YNT_sMnJ_gZx",
        "outputId": "5ffc2196-1dca-417e-fc80-2e79dd378cfa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "\u001b[1m58889256/58889256\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 0us/step\n"
          ]
        }
      ],
      "source": [
        "# load the VGG16 network, ensuring the head FC layers are left off\n",
        "vgg = VGG16(weights=\"imagenet\", include_top=False, input_tensor=Input(shape=(224, 224, 3)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "isysLxtw_gZx"
      },
      "outputs": [],
      "source": [
        "# freeze all VGG layers so they will *not* be updated during the\n",
        "# training process\n",
        "vgg.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "lcqfLyLV_gZx"
      },
      "outputs": [],
      "source": [
        "# flatten the max-pooling output of VGG\n",
        "flatten = vgg.output\n",
        "flatten = Flatten()(flatten)\n",
        "\n",
        "# construct a fully-connected layer header to output the predicted\n",
        "# bounding box coordinates\n",
        "bboxHead = Dense(128, activation=\"relu\")(flatten)\n",
        "bboxHead = Dense(64, activation=\"relu\")(bboxHead)\n",
        "bboxHead = Dense(32, activation=\"relu\")(bboxHead)\n",
        "bboxHead = Dense(4, activation=\"sigmoid\", name=\"bounding_box\")(bboxHead)\n",
        "\n",
        "# construct a second fully-connected layer head, this one to predict\n",
        "# the class label\n",
        "softmaxHead = Dense(512, activation=\"relu\")(flatten)\n",
        "softmaxHead = Dropout(0.5)(softmaxHead)\n",
        "softmaxHead = Dense(512, activation=\"relu\")(softmaxHead)\n",
        "softmaxHead = Dropout(0.5)(softmaxHead)\n",
        "softmaxHead = Dense(len(lb.classes_), activation=\"softmax\", name=\"class_label\")(softmaxHead)\n",
        "\n",
        "# put together our model which accept an input image and then output\n",
        "# bounding box coordinates and a class label\n",
        "model = Model(inputs=vgg.input, outputs=(bboxHead, softmaxHead))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "_1mnXZlN_gZy"
      },
      "outputs": [],
      "source": [
        "# define a dictionary to set the loss methods -- categorical\n",
        "# cross-entropy for the class label head and mean absolute error\n",
        "# for the bounding box head\n",
        "losses = {\n",
        "    \"class_label\": \"categorical_crossentropy\",\n",
        "    \"bounding_box\": \"mean_squared_error\",\n",
        "}\n",
        "\n",
        "# define a dictionary that specifies the weights per loss (both the\n",
        "# class label and bounding box outputs will receive equal weight)\n",
        "lossWeights = {\n",
        "    \"class_label\": 1.0,\n",
        "    \"bounding_box\": 1.0\n",
        "}\n",
        "\n",
        "metrics = {\n",
        "    \"class_label\": \"accuracy\",\n",
        "    \"bounding_box\":None\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "3iOva2AF_gZy"
      },
      "outputs": [],
      "source": [
        "# initialize the optimizer, compile the model, and show the model\n",
        "# summary\n",
        "opt = Adam(learning_rate=1e-4)\n",
        "model.compile(loss=losses, optimizer=opt, metrics=metrics, loss_weights=lossWeights)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qqMOd867_gZy",
        "outputId": "10a01ca5-1e97-4504-a348-7e66733a15ab"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/models/functional.py:237: UserWarning: The structure of `inputs` doesn't match the expected structure.\n",
            "Expected: ['keras_tensor']\n",
            "Received: inputs=Tensor(shape=(None, 224, 224, 3))\n",
            "  warnings.warn(msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m49s\u001b[0m 651ms/step - bounding_box_loss: 0.0251 - class_label_accuracy: 0.8017 - class_label_loss: 0.4489 - loss: 0.4740 - val_bounding_box_loss: 0.0014 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 0.0014 - val_loss: 0.0028\n",
            "Epoch 2/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 201ms/step - bounding_box_loss: 0.0014 - class_label_accuracy: 0.9960 - class_label_loss: 0.0151 - loss: 0.0164 - val_bounding_box_loss: 9.9184e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 2.9359e-04 - val_loss: 0.0013\n",
            "Epoch 3/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 208ms/step - bounding_box_loss: 7.7790e-04 - class_label_accuracy: 1.0000 - class_label_loss: 0.0040 - loss: 0.0048 - val_bounding_box_loss: 8.5475e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.5587e-04 - val_loss: 0.0010\n",
            "Epoch 4/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 207ms/step - bounding_box_loss: 6.0463e-04 - class_label_accuracy: 1.0000 - class_label_loss: 0.0041 - loss: 0.0047 - val_bounding_box_loss: 8.2073e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.5442e-04 - val_loss: 9.8071e-04\n",
            "Epoch 5/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 215ms/step - bounding_box_loss: 4.4335e-04 - class_label_accuracy: 0.9998 - class_label_loss: 0.0029 - loss: 0.0034 - val_bounding_box_loss: 8.2996e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 7.4123e-04 - val_loss: 0.0016\n",
            "Epoch 6/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 214ms/step - bounding_box_loss: 4.0959e-04 - class_label_accuracy: 1.0000 - class_label_loss: 0.0018 - loss: 0.0022 - val_bounding_box_loss: 7.1333e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 4.1124e-05 - val_loss: 7.5985e-04\n",
            "Epoch 7/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 205ms/step - bounding_box_loss: 3.1036e-04 - class_label_accuracy: 1.0000 - class_label_loss: 8.2966e-04 - loss: 0.0011 - val_bounding_box_loss: 7.1696e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.1123e-04 - val_loss: 8.3075e-04\n",
            "Epoch 8/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 215ms/step - bounding_box_loss: 2.8944e-04 - class_label_accuracy: 0.9983 - class_label_loss: 0.0037 - loss: 0.0040 - val_bounding_box_loss: 6.9403e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.7009e-04 - val_loss: 8.6939e-04\n",
            "Epoch 9/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 214ms/step - bounding_box_loss: 2.3652e-04 - class_label_accuracy: 1.0000 - class_label_loss: 0.0012 - loss: 0.0015 - val_bounding_box_loss: 7.1440e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 2.9782e-05 - val_loss: 7.4974e-04\n",
            "Epoch 10/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 204ms/step - bounding_box_loss: 2.1120e-04 - class_label_accuracy: 1.0000 - class_label_loss: 7.0063e-04 - loss: 9.1186e-04 - val_bounding_box_loss: 6.7465e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.6867e-05 - val_loss: 6.9586e-04\n",
            "Epoch 11/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 205ms/step - bounding_box_loss: 1.8368e-04 - class_label_accuracy: 1.0000 - class_label_loss: 6.5578e-04 - loss: 8.3900e-04 - val_bounding_box_loss: 6.8928e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.6447e-05 - val_loss: 7.0931e-04\n",
            "Epoch 12/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 206ms/step - bounding_box_loss: 1.6684e-04 - class_label_accuracy: 1.0000 - class_label_loss: 1.2672e-04 - loss: 2.9353e-04 - val_bounding_box_loss: 7.1639e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.3661e-05 - val_loss: 7.3270e-04\n",
            "Epoch 13/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 204ms/step - bounding_box_loss: 1.6604e-04 - class_label_accuracy: 1.0000 - class_label_loss: 2.1365e-04 - loss: 3.7969e-04 - val_bounding_box_loss: 6.8126e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.4831e-05 - val_loss: 7.0012e-04\n",
            "Epoch 14/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 205ms/step - bounding_box_loss: 1.5211e-04 - class_label_accuracy: 1.0000 - class_label_loss: 9.4055e-04 - loss: 0.0011 - val_bounding_box_loss: 6.7109e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 2.1135e-05 - val_loss: 6.9595e-04\n",
            "Epoch 15/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 216ms/step - bounding_box_loss: 1.3807e-04 - class_label_accuracy: 1.0000 - class_label_loss: 2.0185e-04 - loss: 3.3992e-04 - val_bounding_box_loss: 6.9874e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 2.0496e-05 - val_loss: 7.2307e-04\n",
            "Epoch 16/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 204ms/step - bounding_box_loss: 1.2556e-04 - class_label_accuracy: 1.0000 - class_label_loss: 2.8652e-04 - loss: 4.1210e-04 - val_bounding_box_loss: 6.7426e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 9.7645e-06 - val_loss: 6.8826e-04\n",
            "Epoch 17/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 216ms/step - bounding_box_loss: 1.0984e-04 - class_label_accuracy: 1.0000 - class_label_loss: 1.3159e-04 - loss: 2.4145e-04 - val_bounding_box_loss: 6.4742e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 9.6749e-06 - val_loss: 6.6203e-04\n",
            "Epoch 18/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 204ms/step - bounding_box_loss: 1.1092e-04 - class_label_accuracy: 1.0000 - class_label_loss: 6.1735e-05 - loss: 1.7267e-04 - val_bounding_box_loss: 7.1757e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 6.7835e-06 - val_loss: 7.2824e-04\n",
            "Epoch 19/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 205ms/step - bounding_box_loss: 1.2517e-04 - class_label_accuracy: 1.0000 - class_label_loss: 4.9526e-04 - loss: 6.2050e-04 - val_bounding_box_loss: 6.6367e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.8149e-05 - val_loss: 6.8614e-04\n",
            "Epoch 20/20\n",
            "\u001b[1m51/51\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 206ms/step - bounding_box_loss: 8.9719e-05 - class_label_accuracy: 1.0000 - class_label_loss: 1.0424e-04 - loss: 1.9395e-04 - val_bounding_box_loss: 7.2816e-04 - val_class_label_accuracy: 1.0000 - val_class_label_loss: 1.6834e-05 - val_loss: 7.4755e-04\n"
          ]
        }
      ],
      "source": [
        "# construct a dictionary for our target training outputs\n",
        "trainTargets = {\n",
        "    \"class_label\": trainLabels,\n",
        "    \"bounding_box\": trainBBoxes\n",
        "}\n",
        "\n",
        "# construct a second dictionary, this one for our target testing\n",
        "# outputs\n",
        "testTargets = {\n",
        "    \"class_label\": testLabels,\n",
        "    \"bounding_box\": testBBoxes\n",
        "}\n",
        "\n",
        "# train the network for bounding box regression and class label\n",
        "# prediction\n",
        "H = model.fit(\n",
        "    trainImages, trainTargets,\n",
        "    validation_data=(testImages, testTargets),\n",
        "    batch_size=32,\n",
        "    epochs=20,\n",
        "    verbose=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "AQuw36y6_gZy"
      },
      "outputs": [],
      "source": [
        "model.save('localize.keras')\n",
        "with open('lb3.pickle', \"wb\") as f:\n",
        "    f.write(pickle.dumps(lb))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Up9t5Mj6_gZy",
        "outputId": "771668ff-b3aa-4eff-bf9a-891168acdd53"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x79d11010e6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x1300 with 3 Axes>"
            ],
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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# plot the total loss, label loss, and bounding box loss\n",
        "lossNames = [\"loss\", \"class_label_loss\", \"bounding_box_loss\"]\n",
        "N = np.arange(0, H.params['epochs'])\n",
        "plt.style.use(\"ggplot\")\n",
        "(fig, ax) = plt.subplots(3, 1, figsize=(13, 13))\n",
        "\n",
        "# loop over the loss names\n",
        "for (i, l) in enumerate(lossNames):\n",
        "    # plot the loss for both the training and validation data\n",
        "    title = \"Loss for {}\".format(l) if l != \"loss\" else \"Total loss\"\n",
        "    ax[i].set_title(title)\n",
        "    ax[i].set_xlabel(\"Epoch #\")\n",
        "    ax[i].set_ylabel(\"Loss\")\n",
        "    ax[i].plot(N, H.history[l], label=l)\n",
        "    ax[i].plot(N, H.history[\"val_\" + l], label=\"val_\" + l)\n",
        "    ax[i].legend()\n",
        "\n",
        "# save the losses figure and create a new figure for the accuracies\n",
        "plt.tight_layout()\n",
        "\n",
        "# create a new figure for the accuracies\n",
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(N, H.history[\"class_label_accuracy\"], label=\"class_label_train_acc\")\n",
        "plt.plot(N, H.history[\"val_class_label_accuracy\"], label=\"val_class_label_acc\")\n",
        "plt.title(\"Class Label Accuracy\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend(loc=\"lower left\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJhqM2aI_gZy"
      },
      "source": [
        "Source:\n",
        "\n",
        "https://www.pyimagesearch.com/2020/10/12/multi-class-object-detection-and-bounding-box-regression-with-keras-tensorflow-and-deep-learning/"
      ]
    }
  ],
  "metadata": {
    "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": "3.8.10"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}