Model_MobileNetV2-Data Augmentation+Regularization+Upsampling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3be37ee7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Script for upsampling##\n",
    "\n",
    "\n",
    "# source_folder = r'C:\\Users\\haris\\AI\\ML\\Projects\\Data\\Cars Dataset - Copy\\train'\n",
    "\n",
    "# data = {\n",
    "#     'count': [814, 271, 316, 311, 424, 441, 775],\n",
    "#     'class': ['Audi', 'Hyundai Creta', 'Mahindra Scorpio', 'Rolls Royce', 'Swift', 'Tata Safari', 'Toyota Innova']\n",
    "# }\n",
    "\n",
    "# df = pd.DataFrame(data)\n",
    "\n",
    "# majority_class = df.loc[df['count'].idxmax()]['class']\n",
    "# majority_count = df.loc[df['count'].idxmax()]['count']\n",
    "\n",
    "# desired_total_images = 2000\n",
    "\n",
    "# for index, row in df.iterrows():\n",
    "#     source_class_folder = os.path.join(source_folder, row['class'])\n",
    "#     image_files = os.listdir(source_class_folder)\n",
    "\n",
    "#     current_count = len(image_files)\n",
    "\n",
    "#     images_to_generate = desired_total_images - current_count\n",
    "\n",
    "#     for i in range(images_to_generate):\n",
    "#         random_image_filename = np.random.choice(image_files)\n",
    "#         random_image_path = os.path.join(source_class_folder, random_image_filename)\n",
    "\n",
    "#         random_image = cv2.imread(random_image_path)\n",
    "        \n",
    "#         h, w, _ = random_image.shape\n",
    "#         x = 0 if w <= 224 else np.random.randint(0, w - 224)\n",
    "#         y = 0 if h <= 224 else np.random.randint(0, h - 224)\n",
    "#         cropped_image = random_image[y:y+224, x:x+224]\n",
    "\n",
    "#         output_path = os.path.join(source_class_folder, f'{row[\"class\"]}_{current_count + i}.jpg')\n",
    "#         cv2.imwrite(output_path, cropped_image)\n",
    "\n",
    "#     print(f\"Generated {images_to_generate} augmented images for {row['class']}\")\n",
    "\n",
    "# print(\"Data augmentation completed.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9fafbde8-d270-4d36-8bf8-44b6a68a8ede",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "from tensorflow.keras.applications import MobileNetV2\n",
    "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout,BatchNormalization\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import load_img, img_to_array\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import pandas as pd\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from keras.regularizers import l2\n",
    "from sklearn.utils import class_weight\n",
    "from tensorflow.keras.metrics import Precision, Recall\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import f1_score\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D, BatchNormalization, Conv2D, Dropout, Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b48f7529-c77e-43a6-8d7b-094953f59f60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 14000 images belonging to 7 classes.\n",
      "Found 2800 images belonging to 7 classes.\n",
      "Found 813 images belonging to 7 classes.\n"
     ]
    }
   ],
   "source": [
    "data_dir = r'C:\\Users\\haris\\AI\\ML\\Projects\\Data\\Cars Dataset - Copy'\n",
    "\n",
    "img_size = (224, 224)\n",
    "batch_size = 32\n",
    "\n",
    "datagen = ImageDataGenerator(rescale=1./255,\n",
    "    horizontal_flip = True,                         \n",
    "    rotation_range=20,\n",
    "    width_shift_range=0.1,\n",
    "    height_shift_range=0.1,\n",
    "    fill_mode='nearest',\n",
    "    brightness_range=[0.8, 1.2]\n",
    ")\n",
    "\n",
    "datagen2 = ImageDataGenerator(rescale=1./255, horizontal_flip = False, validation_split=0.2)\n",
    "\n",
    "datagen3 = ImageDataGenerator(rescale=1./255, horizontal_flip = False)\n",
    "\n",
    "train_images = datagen.flow_from_directory(\n",
    "                                          os.path.join(data_dir, 'train'),\n",
    "                                          target_size = img_size,\n",
    "                                          class_mode = \"categorical\",\n",
    "                                          shuffle = True,\n",
    "                                          batch_size = batch_size,\n",
    "                                          subset = 'training')\n",
    "\n",
    "valid_images = datagen2.flow_from_directory(os.path.join(data_dir, 'train'),\n",
    "                                           target_size = img_size,\n",
    "                                           class_mode = \"categorical\",\n",
    "                                           shuffle = False,\n",
    "                                           batch_size = batch_size,\n",
    "                                           subset = 'validation')\n",
    "test_images = datagen3.flow_from_directory(os.path.join(data_dir, 'test'),\n",
    "                                           target_size = img_size,\n",
    "                                           class_mode = \"categorical\",\n",
    "                                           shuffle = False,\n",
    "                                           batch_size = batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af6740ef-0a13-4da2-be2c-5e32a1e51cc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "Epoch 1/100\n",
      "438/438 [==============================] - 136s 297ms/step - loss: 0.7799 - precision: 0.8275 - recall: 0.6495 - accuracy: 0.7291 - val_loss: 0.4156 - val_precision: 0.9021 - val_recall: 0.8225 - val_accuracy: 0.8639\n",
      "Epoch 2/100\n",
      "438/438 [==============================] - 129s 295ms/step - loss: 0.4412 - precision: 0.9000 - recall: 0.8273 - accuracy: 0.8626 - val_loss: 0.2310 - val_precision: 0.9494 - val_recall: 0.9054 - val_accuracy: 0.9229\n",
      "Epoch 3/100\n",
      "438/438 [==============================] - 130s 296ms/step - loss: 0.3286 - precision: 0.9235 - recall: 0.8724 - accuracy: 0.8971 - val_loss: 0.2428 - val_precision: 0.9328 - val_recall: 0.9125 - val_accuracy: 0.9218\n",
      "Epoch 4/100\n",
      "438/438 [==============================] - 131s 299ms/step - loss: 0.2764 - precision: 0.9328 - recall: 0.8949 - accuracy: 0.9134 - val_loss: 0.1842 - val_precision: 0.9562 - val_recall: 0.9268 - val_accuracy: 0.9414\n",
      "Epoch 5/100\n",
      "438/438 [==============================] - 137s 313ms/step - loss: 0.2391 - precision: 0.9444 - recall: 0.9116 - accuracy: 0.9289 - val_loss: 0.1494 - val_precision: 0.9586 - val_recall: 0.9350 - val_accuracy: 0.9493\n",
      "Epoch 6/100\n",
      "438/438 [==============================] - 131s 299ms/step - loss: 0.2158 - precision: 0.9482 - recall: 0.9190 - accuracy: 0.9354 - val_loss: 0.1323 - val_precision: 0.9593 - val_recall: 0.9418 - val_accuracy: 0.9489\n",
      "Epoch 7/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.1878 - precision: 0.9547 - recall: 0.9312 - accuracy: 0.9428 - val_loss: 0.1053 - val_precision: 0.9775 - val_recall: 0.9607 - val_accuracy: 0.9679\n",
      "Epoch 8/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.1735 - precision: 0.9578 - recall: 0.9375 - accuracy: 0.9480 - val_loss: 0.1069 - val_precision: 0.9740 - val_recall: 0.9636 - val_accuracy: 0.9689\n",
      "Epoch 9/100\n",
      "438/438 [==============================] - 129s 295ms/step - loss: 0.1426 - precision: 0.9631 - recall: 0.9467 - accuracy: 0.9556 - val_loss: 0.0841 - val_precision: 0.9783 - val_recall: 0.9671 - val_accuracy: 0.9725\n",
      "Epoch 10/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.1439 - precision: 0.9645 - recall: 0.9486 - accuracy: 0.9572 - val_loss: 0.1048 - val_precision: 0.9703 - val_recall: 0.9582 - val_accuracy: 0.9650\n",
      "Epoch 11/100\n",
      "438/438 [==============================] - 133s 303ms/step - loss: 0.1374 - precision: 0.9682 - recall: 0.9521 - accuracy: 0.9605 - val_loss: 0.0768 - val_precision: 0.9823 - val_recall: 0.9707 - val_accuracy: 0.9757\n",
      "Epoch 12/100\n",
      "438/438 [==============================] - 134s 306ms/step - loss: 0.1185 - precision: 0.9719 - recall: 0.9584 - accuracy: 0.9656 - val_loss: 0.0592 - val_precision: 0.9828 - val_recall: 0.9775 - val_accuracy: 0.9811\n",
      "Epoch 13/100\n",
      "438/438 [==============================] - 133s 304ms/step - loss: 0.1133 - precision: 0.9714 - recall: 0.9586 - accuracy: 0.9652 - val_loss: 0.0634 - val_precision: 0.9813 - val_recall: 0.9721 - val_accuracy: 0.9771\n",
      "Epoch 14/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.1182 - precision: 0.9714 - recall: 0.9591 - accuracy: 0.9657 - val_loss: 0.0741 - val_precision: 0.9791 - val_recall: 0.9704 - val_accuracy: 0.9754\n",
      "Epoch 15/100\n",
      "438/438 [==============================] - 130s 296ms/step - loss: 0.1130 - precision: 0.9743 - recall: 0.9622 - accuracy: 0.9686 - val_loss: 0.0604 - val_precision: 0.9823 - val_recall: 0.9739 - val_accuracy: 0.9782\n",
      "Epoch 16/100\n",
      "438/438 [==============================] - 130s 298ms/step - loss: 0.0994 - precision: 0.9752 - recall: 0.9652 - accuracy: 0.9708 - val_loss: 0.0664 - val_precision: 0.9842 - val_recall: 0.9779 - val_accuracy: 0.9829\n",
      "Epoch 17/100\n",
      "438/438 [==============================] - 131s 298ms/step - loss: 0.1072 - precision: 0.9754 - recall: 0.9646 - accuracy: 0.9701 - val_loss: 0.0608 - val_precision: 0.9860 - val_recall: 0.9800 - val_accuracy: 0.9829\n",
      "Epoch 18/100\n",
      "438/438 [==============================] - 130s 296ms/step - loss: 0.0941 - precision: 0.9776 - recall: 0.9683 - accuracy: 0.9729 - val_loss: 0.0402 - val_precision: 0.9896 - val_recall: 0.9850 - val_accuracy: 0.9882\n",
      "Epoch 19/100\n",
      "438/438 [==============================] - 130s 298ms/step - loss: 0.0899 - precision: 0.9784 - recall: 0.9694 - accuracy: 0.9738 - val_loss: 0.0675 - val_precision: 0.9864 - val_recall: 0.9814 - val_accuracy: 0.9832\n",
      "Epoch 20/100\n",
      "438/438 [==============================] - 132s 301ms/step - loss: 0.0867 - precision: 0.9795 - recall: 0.9705 - accuracy: 0.9757 - val_loss: 0.0599 - val_precision: 0.9835 - val_recall: 0.9804 - val_accuracy: 0.9811\n",
      "Epoch 21/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.0921 - precision: 0.9778 - recall: 0.9691 - accuracy: 0.9741 - val_loss: 0.0484 - val_precision: 0.9864 - val_recall: 0.9814 - val_accuracy: 0.9846\n",
      "Epoch 22/100\n",
      "438/438 [==============================] - 131s 298ms/step - loss: 0.0902 - precision: 0.9798 - recall: 0.9719 - accuracy: 0.9760 - val_loss: 0.0544 - val_precision: 0.9871 - val_recall: 0.9800 - val_accuracy: 0.9839\n",
      "Epoch 23/100\n",
      "438/438 [==============================] - 131s 298ms/step - loss: 0.0796 - precision: 0.9825 - recall: 0.9748 - accuracy: 0.9797 - val_loss: 0.0401 - val_precision: 0.9907 - val_recall: 0.9889 - val_accuracy: 0.9907\n",
      "Epoch 24/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.0852 - precision: 0.9803 - recall: 0.9719 - accuracy: 0.9766 - val_loss: 0.0297 - val_precision: 0.9932 - val_recall: 0.9911 - val_accuracy: 0.9925\n",
      "Epoch 25/100\n",
      "438/438 [==============================] - 131s 298ms/step - loss: 0.0780 - precision: 0.9822 - recall: 0.9756 - accuracy: 0.9789 - val_loss: 0.0366 - val_precision: 0.9907 - val_recall: 0.9864 - val_accuracy: 0.9889\n",
      "Epoch 26/100\n",
      "438/438 [==============================] - 132s 301ms/step - loss: 0.0692 - precision: 0.9835 - recall: 0.9776 - accuracy: 0.9801 - val_loss: 0.0679 - val_precision: 0.9828 - val_recall: 0.9789 - val_accuracy: 0.9800\n",
      "Epoch 27/100\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.0736 - precision: 0.9836 - recall: 0.9759 - accuracy: 0.9797 - val_loss: 0.0515 - val_precision: 0.9857 - val_recall: 0.9814 - val_accuracy: 0.9839\n",
      "Epoch 28/100\n",
      "438/438 [==============================] - 131s 298ms/step - loss: 0.0733 - precision: 0.9828 - recall: 0.9755 - accuracy: 0.9792 - val_loss: 0.0310 - val_precision: 0.9921 - val_recall: 0.9893 - val_accuracy: 0.9914\n",
      "Epoch 29/100\n",
      "438/438 [==============================] - ETA: 0s - loss: 0.0699 - precision: 0.9822 - recall: 0.9756 - accuracy: 0.9790Restoring model weights from the end of the best epoch: 24.\n",
      "438/438 [==============================] - 130s 297ms/step - loss: 0.0699 - precision: 0.9822 - recall: 0.9756 - accuracy: 0.9790 - val_loss: 0.0455 - val_precision: 0.9882 - val_recall: 0.9829 - val_accuracy: 0.9854\n",
      "Epoch 29: early stopping\n"
     ]
    }
   ],
   "source": [
    "base_model = MobileNetV2(weights='imagenet', include_top=False)\n",
    "\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = BatchNormalization() (x)\n",
    "x = Dense(1024, activation = \"relu\") (x)\n",
    "x = Dropout(0.3) (x)\n",
    "x = Dense(512, activation = \"relu\") (x)\n",
    "x = Dropout(0.25) (x)\n",
    "x = Dense(128, activation = \"relu\") (x)\n",
    "x = Dropout(0.15) (x)\n",
    "x = Dense(64, activation = \"relu\") (x)\n",
    "x = Dropout(0.15) (x)\n",
    "predictions = Dense(7, activation='softmax')(x)\n",
    "\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "model.compile(\n",
    "    optimizer=Adam(learning_rate=0.001),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=[Precision(name='precision'), Recall(name='recall'), 'accuracy']\n",
    ")\n",
    "\n",
    "early_stopping = EarlyStopping(monitor=\"val_accuracy\", patience  = 5,verbose=1, restore_best_weights=True)\n",
    "\n",
    "history = model.fit(\n",
    "    train_images,\n",
    "    validation_data=valid_images,\n",
    "    batch_size=batch_size,\n",
    "    epochs=100,\n",
    "    callbacks=[early_stopping]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "04c6cf05-3649-4090-8f37-2dacf146b38b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(history.history['accuracy'], label='Train Accuracy')  \n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(history.history['loss'], label='Train Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "adbf791d-609e-4a0e-8565-a5fb7736d23c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26/26 [==============================] - 1s 41ms/step - loss: 0.3882 - precision: 0.9396 - recall: 0.9188 - accuracy: 0.9287\n",
      "Test Loss: 0.3881571292877197\n",
      "Test Accuracy: 0.9396226406097412\n",
      "26/26 [==============================] - 2s 40ms/step\n",
      "Classification Report:\n",
      "                   precision    recall  f1-score   support\n",
      "\n",
      "            Audi       0.92      0.93      0.93       199\n",
      "   Hyundai Creta       0.93      0.82      0.87        67\n",
      "Mahindra Scorpio       0.96      0.92      0.94        75\n",
      "     Rolls Royce       0.88      0.82      0.85        74\n",
      "           Swift       0.90      0.93      0.91       102\n",
      "     Tata Safari       0.95      0.96      0.96       106\n",
      "   Toyota Innova       0.95      0.98      0.97       190\n",
      "\n",
      "        accuracy                           0.93       813\n",
      "       macro avg       0.93      0.91      0.92       813\n",
      "    weighted avg       0.93      0.93      0.93       813\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "true_classes = test_images.classes\n",
    "class_names = list(test_images.class_indices.keys())\n",
    "test_evaluation = model.evaluate(test_images)\n",
    "test_loss = test_evaluation[0]\n",
    "test_accuracy = test_evaluation[1]\n",
    "\n",
    "print(f\"Test Loss: {test_loss}\")\n",
    "print(f\"Test Accuracy: {test_accuracy}\")\n",
    "\n",
    "test_predictions = model.predict(test_images)\n",
    "test_predictions_classes = np.argmax(test_predictions, axis=1)\n",
    "confusion_mat = confusion_matrix(true_classes, test_predictions_classes)\n",
    "classification_rep = classification_report(true_classes, test_predictions_classes, target_names=class_names)\n",
    "print(\"Classification Report:\\n\", classification_rep)\n",
    "\n",
    "import seaborn as sn\n",
    "df_cm = pd.DataFrame(confusion_mat, index=class_names, columns=class_names)\n",
    "sn.heatmap(df_cm, annot=True, fmt=\"d\", cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d914c3f2-8da5-4706-9e4e-289dc2e93849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully.\n"
     ]
    }
   ],
   "source": [
    "model.save(\"car_classification_model.h5\")\n",
    "print(\"Model saved successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2197931c-16c8-4bae-bbd0-c33746d15c21",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Audi', 'Hyundai Creta', 'Mahindra Scorpio', 'Rolls Royce', 'Swift', 'Tata Safari', 'Toyota Innova']\n"
     ]
    }
   ],
   "source": [
    "print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4057db62-6f26-44e8-8a40-2b60fb625766",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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