Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyP7IHmDhpl4ndcR4FpwJQgS",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MahanNasirkhani/Mask-Detection/blob/main/Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "7Tbq7uTNn9Nr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a8d96918-7296-4da9-8323-fe2e92a870ad"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import optimizers\n",
        "from keras.applications import VGG16"
      ],
      "metadata": {
        "id": "ipWx97WG1GAK"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load Dateset"
      ],
      "metadata": {
        "id": "Ap6IgWxPJhBy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "PfRPoe4RJVJm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir = '/content/drive/MyDrive/Mask Dataset/Train'\n",
        "test_dir = '/content/drive/MyDrive/Mask Dataset/Test'"
      ],
      "metadata": {
        "id": "p48vh4GqoHx9"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n",
        "img_height = 100\n",
        "img_width = 100"
      ],
      "metadata": {
        "id": "k4oDcwGV4UZ2"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  train_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"training\",\n",
        "  shuffle=True,\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)\n",
        "\n",
        "val_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  train_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"validation\",\n",
        "  shuffle=True,\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  test_dir,\n",
        "  shuffle=True,\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "metadata": {
        "id": "0LQgRiSU4ZjV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c8a76eee-b9ac-44b4-e5fb-fddfb8cc10c4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 4926 files belonging to 2 classes.\n",
            "Using 3941 files for training.\n",
            "Found 4926 files belonging to 2 classes.\n",
            "Using 985 files for validation.\n",
            "Found 1026 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "test_ds_1 = test_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "metadata": {
        "id": "uUhtUzEU4ySC"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Augmentation layers"
      ],
      "metadata": {
        "id": "5vC365sfJlZv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "  [\n",
        "    layers.RandomFlip(\"horizontal_and_vertical\",input_shape=(img_height, img_width, 3)),\n",
        "    layers.RandomRotation(0.1),\n",
        "    layers.RandomZoom(0.1),\n",
        "    layers.RandomTranslation(height_factor = 0.1, width_factor = 0.1 , fill_value=0.1),\n",
        "    layers.RandomContrast(factor = 0.1),\n",
        "    layers.RandomBrightness(factor = 0.1)\n",
        "  ]\n",
        ")"
      ],
      "metadata": {
        "id": "mUoI-g6J6u9n"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# VGG16 model and layers"
      ],
      "metadata": {
        "id": "7uhG0E25OzTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base = VGG16(weights='imagenet',\n",
        "                  include_top=False,\n",
        "                  input_shape=(100, 100, 3))"
      ],
      "metadata": {
        "id": "a4H_63ps5uRg"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "conv_base.trainable = True\n",
        "\n",
        "set_trainable = False\n",
        "for layer in conv_base.layers:\n",
        "    if layer.name == 'block5_conv1':\n",
        "        set_trainable = True\n",
        "    if layer.name == 'block4_conv1':\n",
        "        set_trainable = True\n",
        "    if set_trainable:\n",
        "        layer.trainable = True\n",
        "    else:\n",
        "        layer.trainable = False"
      ],
      "metadata": {
        "id": "MIr-0Oos53GN"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model"
      ],
      "metadata": {
        "id": "cGXQWTfUO3zu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.Sequential()\n",
        "model.add(data_augmentation)\n",
        "model.add(layers.Rescaling(1./255))\n",
        "model.add(conv_base)\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(128, activation='relu'))\n",
        "model.add(layers.Dropout(0.4))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "DsBgH5gE56h0"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "L-7_sTp36EcP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5277a2e2-7baa-4c6e-e15f-8fdc75ce1643"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " sequential_6 (Sequential)   (None, 100, 100, 3)       0         \n",
            "                                                                 \n",
            " rescaling_3 (Rescaling)     (None, 100, 100, 3)       0         \n",
            "                                                                 \n",
            " vgg16 (Functional)          (None, 3, 3, 512)         14714688  \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 4608)              0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               589952    \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 15,304,769\n",
            "Trainable params: 13,569,281\n",
            "Non-trainable params: 1,735,488\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer=optimizers.RMSprop(learning_rate = 2e-5),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "UGNGdS7Y6Fxb"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "aKr4SjTQO7Xp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.callbacks import ModelCheckpoint\n",
        "\n",
        "checkpoint = ModelCheckpoint('best_model.h5', save_best_only=True, monitor='val_loss', mode='min')"
      ],
      "metadata": {
        "id": "_gExdDLsg0Fs"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 10\n",
        "history = model.fit(train_ds, validation_data=val_ds, epochs=epochs, callbacks=[checkpoint])"
      ],
      "metadata": {
        "id": "nVe-Toz46HIs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eca2390b-1d7c-435a-e16f-0f2cc7949f43"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "124/124 [==============================] - 11s 78ms/step - loss: 0.1584 - accuracy: 0.9355 - val_loss: 0.0609 - val_accuracy: 0.9827\n",
            "Epoch 2/10\n",
            "124/124 [==============================] - 9s 74ms/step - loss: 0.0574 - accuracy: 0.9789 - val_loss: 0.0625 - val_accuracy: 0.9858\n",
            "Epoch 3/10\n",
            "124/124 [==============================] - 9s 77ms/step - loss: 0.0498 - accuracy: 0.9843 - val_loss: 0.0521 - val_accuracy: 0.9878\n",
            "Epoch 4/10\n",
            "124/124 [==============================] - 9s 76ms/step - loss: 0.0337 - accuracy: 0.9904 - val_loss: 0.0565 - val_accuracy: 0.9878\n",
            "Epoch 5/10\n",
            "124/124 [==============================] - 10s 81ms/step - loss: 0.0399 - accuracy: 0.9896 - val_loss: 0.0746 - val_accuracy: 0.9848\n",
            "Epoch 6/10\n",
            "124/124 [==============================] - 10s 77ms/step - loss: 0.0268 - accuracy: 0.9906 - val_loss: 0.0445 - val_accuracy: 0.9929\n",
            "Epoch 7/10\n",
            "124/124 [==============================] - 10s 78ms/step - loss: 0.0245 - accuracy: 0.9919 - val_loss: 0.0508 - val_accuracy: 0.9929\n",
            "Epoch 8/10\n",
            "124/124 [==============================] - 9s 75ms/step - loss: 0.0269 - accuracy: 0.9899 - val_loss: 0.0487 - val_accuracy: 0.9898\n",
            "Epoch 9/10\n",
            "124/124 [==============================] - 9s 72ms/step - loss: 0.0247 - accuracy: 0.9934 - val_loss: 0.0633 - val_accuracy: 0.9888\n",
            "Epoch 10/10\n",
            "124/124 [==============================] - 9s 74ms/step - loss: 0.0152 - accuracy: 0.9957 - val_loss: 0.0602 - val_accuracy: 0.9888\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Plotting accuracy and loss"
      ],
      "metadata": {
        "id": "bI_S6WK1O_Wg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "yFsMhK0s6bm8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "outputId": "8f5598a8-87d2-452d-ecbb-3a3273517afa"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing the model"
      ],
      "metadata": {
        "id": "5NfkoNvkPEYh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "best_model = load_model('best_model.h5')"
      ],
      "metadata": {
        "id": "rYDuJ-5Fg9mx"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loss, acc = best_model.evaluate(test_ds_1)\n",
        "print('\\nTesting loss: %.2f, acc: %.2f%%'%(loss, acc))"
      ],
      "metadata": {
        "id": "TUvaW0cl6cNw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43447ded-4d18-4dca-b989-adcdef8a2880"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "33/33 [==============================] - 1s 33ms/step - loss: 0.1838 - accuracy: 0.9522\n",
            "\n",
            "Testing loss: 0.18, acc: 0.95%\n"
          ]
        }
      ]
    }
  ]
}