train_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "train_cnn.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/iamdsc/automatic-helmet-detection/blob/master/train_cnn.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztDcaOpnk-I0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# mounting google drive to colab to get the training data\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XEFDklKRoJRL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%cp -r 'drive/My Drive/images.zip' 'images.zip'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yv9eWsj1MCgh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!unzip images.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcNCMCrB_yXX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import os\n",
        "import cv2\n",
        "import random\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, BatchNormalization\n",
        "from keras.optimizers import RMSprop"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sB4EujpEZszY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATADIR = 'images'\n",
        "CLASS = ['neg','pos']\n",
        "IMG_SIZE = 50\n",
        "\n",
        "neg = []\n",
        "pos = []\n",
        "\n",
        "# building the training data\n",
        "def create_training_data():  \n",
        "  for cl in CLASS:\n",
        "    path = os.path.join(DATADIR, cl)\n",
        "    class_num = CLASS.index(cl)\n",
        "    if class_num == 0:\n",
        "      for img in os.listdir(path):\n",
        "        try:\n",
        "          img_array = cv2.imread(os.path.join(path, img))\n",
        "          img_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
        "          gray_img = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)\n",
        "          neg.append([gray_img, class_num])\n",
        "        except Exception as e:\n",
        "          pass\n",
        "    if class_num == 1:\n",
        "      for img in os.listdir(path):\n",
        "        try:\n",
        "          img_array = cv2.imread(os.path.join(path, img))\n",
        "          img_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
        "          gray_img = cv2.cvtColor(img_array, cv2.COLOR_BGR2GRAY)\n",
        "          pos.append([gray_img, class_num])\n",
        "        except Exception as e:\n",
        "          pass\n",
        "  random.shuffle(neg)\n",
        "  random.shuffle(pos)\n",
        "  training_data = neg[:len(pos)]+pos\n",
        "  return training_data\n",
        "\n",
        "training_data = create_training_data()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8FQtkcJ4lMi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#shuffle the data\n",
        "random.shuffle(training_data)\n",
        "print(len(training_data))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D9gH6_Iq8RN6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = []\n",
        "y = []\n",
        "\n",
        "for features, label in training_data:\n",
        "  X.append(features)\n",
        "  y.append(label)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFhckiqH936A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RP0EUPt1-ByP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# normalizing the data\n",
        "X = X/255.0"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2P4ee8Q4-ZNV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# building the model\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=X.shape[1:], data_format='channels_last',))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XW0GOWIGAFdM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pA0tAy8hAMXx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "history = model.fit(X, y, batch_size=32, validation_split=0.1, epochs=15)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jknQYiOXBWIS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# helper function to plot the results\n",
        "def plot_result(history):\n",
        "  acc = history.history['acc']\n",
        "  val_acc = history.history['val_acc']\n",
        "  loss = history.history['loss']\n",
        "  val_loss = history.history['val_loss']\n",
        "  \n",
        "  epochs = range(1, len(acc)+1)\n",
        "\n",
        "  plt.plot(epochs, acc, label='Training acc')\n",
        "  plt.plot(epochs, val_acc, label='Validation acc')\n",
        "  plt.title('Training and validation accuracy')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.ylabel('acc')\n",
        "  plt.legend()\n",
        "\n",
        "  plt.figure()\n",
        "\n",
        "  plt.plot(epochs, loss, label='Training loss')\n",
        "  plt.plot(epochs, val_loss, label='Validation loss')\n",
        "  plt.title('Training and validation loss')\n",
        "  plt.xlabel('epochs')\n",
        "  plt.ylabel('loss')\n",
        "  plt.legend()\n",
        "  \n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DySI4hkfB4Rl",
        "colab_type": "code",
        "outputId": "3a8646fb-7aac-4a2d-cf71-37adec6f6257",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        }
      },
      "source": [
        "plot_result(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zpYnUldB7il3HIcKZSLLLmXYW1uVPQdezoPdkd2P1ZAx8cDPkZ9k2ipAItyPy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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gnLO3C+C1J2OCLQAY7OYxpVSnBTIprAbGiEiWiEQDVwAvt9jn79irBERkELY5\n6ZMAxgRA8sBoxg5JYPXuEOtsBls5taEGtrT8VQXI/vW2ySqQTUfNMnKhvhrKdW0FpUJVwJKCMcYL\n3AgsB7YCzxljNovIHSJykbPbcqBURLYAK4AfGmNKAxWTv/ysFNbuLqexKUQW3Wk2Yjqkjg7eKKRA\nFMBri6/chS7PqVSoCmifgjFmmTHmRGPM8caYO51ttxljXnbuG2PMzcaYbGNMrjFmaSDj8ZefmUp1\nnZet+yuDdcrOaZ6zUPierUUUaNsDUACvLenjQSK0X0GpEOZ2R7NrpjmL7oRcyQuAiZfb25duhPLC\nwJ3n8B77AR2MpiOAqFi76I4mBaVCVr9NCsclDWB4yoDQm68AkDIKzv8/KCqAxSfDu3eDt673zxOo\nAnjt0bUVlApp/TYpgB2aurqwDGNCrF8BIP86uHGVLa399s/hwRnwyTu9e45tr8KgE3u/AF57hkyw\nK7sdCUrXkVKqi/p1UsjPSuVQdT27Dh1xO5TWJQ2Hy5+CK5+HpgZ48iJ44TqoOtDzY/sK4AWp6aiZ\nrq2gVEjrVFIQke+ISKJYfxKRtSJydqCDC7T8TGe+Qig2Ifkb80X45ocw80ew5SX4fT6sfBiaejAj\nO9AF8NriK3ehI5CUCkWdvVL4qjGmEjgbSAGuAu4KWFRBcnx6PKlx0awuDMH5Ci1FDYBZt9jkMOwk\neO3/wSOn236H7tj2KsSlB64AXlviBkHCUO1XUCpEdTYpiHN7HvCUMWaz37awJSLkjUoJ/SsFf2nH\nw1UvwqVPwJESePQseOW7XVu1rbkA3okBLIDXHu1sVipkdfYTYY2I/BObFJaLSALQJ2oVTMtKZXdp\nDcWVtW6H0nkikDMXvrUKTv4mrH3SNimte8aWrejI7vdtAbxgNx01y8iFQ9uhIYx+50r1E51NCtcC\ni4B8Y0wNEAVcE7Cogig/05mvEE5XC81iE2H2L+Hr79hZ0H+/AR4/Dw5uaf9125YFvgBeezIm2P6M\nkm3unF8p1abOJoVTgO3GmMMishC4FagIXFjBkzM0kYHREaFXHK8rMnLhq8vhogegZCs8dCr881ao\nq/78vsEsgNeWjIn2VpuQlAo5nU0KDwI1IjIJ+D7wMfBkwKIKosgID1NHprAqHDqb2+PxwNQvw41r\nbFG9fz8Ai6fZwnr+TUoHNjgF8AK0FnNnpGRBVJzWQFIqBHU2KXiNneE1B/i9MWYxkBC4sIIrPzOV\nbQcqqTja4HYoPReXBnN+D1/9JwxIgeeugiWXQplTfHZbcwG8VtczCg6PxzYh6ZWCUiGns0mhSkR+\njB2K+qqIeLD9Cn1CfmYKxsBzLfWkAAAdxUlEQVTaUCyl3V0jp8P178A5v4I9/4E/nALv/Aa2/SN4\nBfDa0zwCKRRnkyvVj3U2KVwO1GHnKxzALpjz24BFFWRTRqYQ6ZHw7GxuT0QknPJNuHG1bS5acadt\nsgn2LObWZOTaEVCHd7sdiVLKT6eSgpMIlgBJInIBUGuM6RN9CgADoiOYMCyJgr6WFJolDrXzGhb+\nDXIugUlXuB0RDGme2axNSEqFks6WubgMWAVcClwGrBSReYEMLNimZaWyfm8FtQ09KB0R6k44Ey59\nHOIHux0JDB5v+zY0KSgVUjrbfPQT7ByFrxhjvgxMA/43cGEFX35mKvWNTWwo6hMjbUNf9EBIG6M1\nkJQKMZ1NCh5jTLHf49IuvDYs5I0Kk+J4fYmWu1Aq5HT2g/11EVkuIleLyNXAq8CywIUVfClx0Zw4\nJD40V2LrqzJyoWIPHO1Do76UCnOd7Wj+IfAIMNH5ecQY86NABuaG/MxU1u4up7FJh0kGhZbRVirk\ndLoJyBjzgjHmZufnxUAG5Zb8zFSq6rxs3V/pdij9Q4aOQFIq1ES296SIVAGtfW0WwBhjEgMSlUvy\ns2xxvNWFZUwYluRyNP1A/GCIH6JJQakQ0u6VgjEmwRiT2MpPQl9LCADDkgcwLHkABeFeBymcZOTq\n0pxKhZA+NYKoN+RnprCqsAyj5ReCIyMXirfZhX+UUq7TpNBCflYqJVV17C6tcTuU/iEjF5oa7KI7\nSinXaVJoYVo4L7oTjnRtBaVCiiaFFk4YHE/KwKjwXnQnnKSOtqvAaVJQKiRoUmhBRMjLTNWZzcHi\niYDB2ZoUlAoRmhRakZ+ZQmFpDcVVurB8UGTk2hXhtHNfKddpUmhFvtOvsHqXDk0NioxcqK2AiiK3\nI1Gq39Ok0IoJw5IYEBWhTUjBop3NSoUMTQqtiIrwMGVksiaFYBmSDYgmBaVCQECTgojMFpHtIrJT\nRBa1s9+XRMSISF4g4+mK/MxUtu6vpKq2we1Q+r7oOEg7wfYrKKVcFbCkICIRwGLgXCAbmC8i2a3s\nlwB8B1gZqFi6Y1pWKk0G1uzWfoWgyJigVwpKhYBAXilMA3YaYz4xxtQDS4E5rez3c+DXQEgN9Zky\nMplIj2gTUrBk5MLh3bbDWSnlmkAmhWHAXr/HRc42HxGZCowwxrza3oFE5HoRKRCRgpKSkt6PtBUD\noyPJGZakI5CCpbmz+eBmd+NQqp9zraNZRDzAPcD3O9rXGPOIMSbPGJOXnp4e+OAc+aNSWFd0mDpv\nY9DO2W/p2gpKhYRAJoV9wAi/x8Odbc0SgAnAv0SkEDgZeDmkOpuzUqn3NrGhSJs0Ai5+CMSla2ez\nUi4LZFJYDYwRkSwRiQauAF5uftIYU2GMGWSMyTTGZAIfAhcZYwoCGFOXNE9i03Wbg0DEmdmsVwpK\nuSlgScEY4wVuBJYDW4HnjDGbReQOEbkoUOftTalx0ZwwOJ4C7WwOjiEToHgrNOowYKXc0u5ynD1l\njFkGLGux7bY29j09kLF0V35mKv/Y8CmNTYYIj7gdTt+WMREa6+HQRzAkx+1olOqXdEZzB6ZlpVBV\n62X7gSq3Q+n7fJ3Nm9yNQ6l+TJNCB3zF8bQJKfDSToDIWO1sVspFmhQ6MDxlIEOTYnUltmCIiNS1\nFZRymSaFTsjLTGX1rjKM1vsPvOYRSPq7VsoVmhQ6IT8rleKqOvaU1bgdSt+XkQtHy6DyU7cjUapf\n0qTQCdN0vkLw6MxmpVylSaETxgyOJ2lAFAWFWgcp4JqHoh7UpKCUGzQpdILHI+RnpugIpGCISYDU\n0XqloJRLNCl0Un5mKp8cOkJJVZ3bofR9Wu5CKddoUuik/Czbr6AlL4IgIxfKPoE6nTCoVLBpUuik\nCUOTiI3y6HyFYNC1FZRyjSaFToqO9DB5RLL2KwTDkAn2VpuQlAo6TQpdMC0zlS2fVlJVq1U8Aypx\nKAxI1aSglAs0KXRBflYqTQbW7jnsdih9m66toJRrNCl0wdSRKUR4hNU6iS3wMnKheAs0et2ORKl+\nRZNCF8TFRJIzNFH7FYIhYyJ4a6F0p9uRKNWvaFLoovzMVNbtPUydt9HtUPo2LXehlCs0KXRRfmYq\ndd4mNu2rcDuUvm3QGIiI1rUVlAoyTQpdlJeZAsCqXVoHKaAiomwdpE1/g/3r3Y5GqX5Dk0IXDYqP\nYXR6nPYrBMO5vwXTBI+eBav+qGssKBUEmhS6YVpmKgWFZTQ16YdUQI3Ih2+8D6NnwbIfwF8WwlG9\nQlMqkDQpdEN+ZiqVtV62H9TaPAEXlwbzl8LZv4CPXoeHvgB7V7sdlVJ9liaFbpjmFMfTJqQg8Xjg\nf74NX10OAjw+Gz74HTQ1uR2ZUn2OJoVuGJ4ygIzEWFbrojvBNTwPvv4ejDsf3rgNnrkMjhxyOyql\n+hRNCt0gIuRnpbJ6VxlGOz+Da0AyXPpnOP//YNe78NCpsOs9t6NSqs/QpNBN0zJTOFBZS1H5UbdD\n6X9EIP86+NpbEB0HT14E/7oLmnRCoVI9pUmhm/Iybb/CKq2D5J6MXLj+Hci9DP71K3hyDlTudyeW\nuirYt1b7OVTY06TQTWOHJJAYG6mdzW6LiYdLHoaLH4R9a2xz0o43g3PuumrY+DwsvRJ+ewL8cRYs\n+RJUFwfn/EoFgCaFbvJ4hLzMVF2JLVRMXgDX/wvih9gP5jdug8YArHtRfwQ2vwh/ucomgheuhaLV\nMPUrcOZPYfe/4cEZ8PHbvX9upYIg0u0Awll+ZipvbyvmUHUdg+Jj3A5HpY+1/Qyv/9gOWd39b/jS\nnyBlVM+OW18DO9+wyeCj5dBQA3GDYcpCyJkLI08GT4Td98Rz4K/XwFOXwKnfhVk/sSU7lAoTmhR6\nYFqWrYNUUFjG7AnHuRyNAiBqAFx4H2R9AV75Djx8GsxZDOMv7NpxGo7CzjdtItj+OjQcgYGDYNJ8\nmwhG/c9nicDfkBx7xfL6Inj/Xih8v3cSk1JBokmhB3KHJRMT6eGdj0o45fhBJMZGIiJuh6UAJlwC\nQ6fA89fY8hjTrocv/hyiYtt+TUMtfPyWkwheg/pqGJgGEy9zEsEMiOjEn0z0QLjofhg9E175Ljx0\nmn2cc3HvvT+lAkQCOc5eRGYDvwMigEeNMXe1eP5m4DrAC5QAXzXG7G7vmHl5eaagoCBAEXfdlY9+\nyAc7SwGIjfIwJDGWwQkxDE6MZUhCLEMSY+w253ZIYizxMZqLg8ZbD2/eDh8utgv3XPoEpB3v93yd\nbf/f/CJsWwb1VXZ96PEX2kSQeVrnEkFbynbZfod9a+Cka2D2r+zVjFJBJiJrjDF5He4XqKQgIhHA\nR8AXgSJgNTDfGLPFb59ZwEpjTI2I3ACcboy5vL3jhlpSOFhZy8pdZRRX1lJcVcfByloOVtZSXFnH\ngcpaauo/P3Z+YHSEL3nYRNGcOGIZkvBZEhkYrcmj12x/Df5+g+18Pv//YECKkwhehbpKiE3+LBFk\nfaF3+wG89fD2z+Hf98PgbJj3GAwe33vHV6oTQiEpnALcbow5x3n8YwBjzK/a2H8K8HtjzIz2jhtq\nSaEj1XXeYxJFcVUtByvrfI8PVtnnahs+P7596shkFkwfxQUTjyM2qpX2a9U1FUXw/LWw90P7ODYJ\nxjmJYPTMwHcI73wT/vZ1O4Lp3F/D1C/biXhKBUEoJIV5wGxjzHXO46uA6caYG9vY//fAAWPML1p5\n7nrgeoCRI0eetHt3uy1MYccYQ2Wt95irjb1lR3lp/T4+KTlC0oAovjR1OAumj+SEwfFuhxveGr2w\n8TnbaTz6dIiMDu75qw7A366HXe/YZHTh72xyUm1rbLAl0+MHux1JWAurpCAiC4EbgZnGmLr2jhtu\nVwo9YYzhw0/KWLJyN8s3H6Ch0TA9K5UrTx7FOTlDiInUq4ew1NQEH9wLb98JScNtc9LwDv9W+76m\nJqjYA8Vb4eBme1u8FQ59BE0NMHSqHQacO08TaTeEQlLoVPORiJwFPIBNCB1OBe1PScHfoeo6/lpQ\nxDOrdrO37ChpcdFcmjeCBdNGMjJtoNvhqe7YsxJeuA6qPoUzb4NTvm3LhPcH1SVQvOWzn4NboGSb\nHfHVLGmk7XsZPB5iE+3s8eItEDkAsufA1KvsiDBtguuUUEgKkdiO5jOBfdiO5gXGmM1++0wBnsde\nUezozHH7a1Jo1tRkeG/nIZ5ZuZs3txbT2GQ4bcwgrpw+ijPHDyYqop98qPQVRw/Dy9+GrS/D8WfC\n3If6VjNJXRUUb/t8AqjxK3k+MM12wA/OtglgSA6kj7OJwJ8xtr7Uf5+CTS/YAQKpo+3Vw6QFkKhz\nhdrjelJwgjgPuA87JPUxY8ydInIHUGCMeVlE3gRygeYqZnuMMRe1d8z+nhT8Haio5S+r97J09R72\nV9QyOCGGK/JHcPm0kQxL1mGPYcMYWPO4nYkdkwiXPALHz3I7qs5rbIDKT6FyHxzea7/xNyeAw3s+\n2y8qDgaP+3wCiEvv+rf9+hrY8pJNELs/APHAmLNhylV2VrnOIv+ckEgKgaBJ4fO8jU38a3sJS1bu\n5l8flSDAGeMGs2D6SGaeOJgIj15eh4WDm22JjEMfwanfg1m3uP/hZoxdyKiyyI7eqtgHFXvt/cp9\n9rbqAOD3OeKJhEEnOk0/fgkgeVRgmsdKP7bJYd2zUH3AJplJV8CUL0P6ib1/vjClSaGf2ltWw9LV\ne/jL6iIOVdcxLHkA86eN4LK8EQxObGc2rwoN9TW2RMbaP8PwafClRwNbIqOu+rMP9+afyuYP/n32\nvrf22NdExtoO8sRhkDTC3k8a5mwbDimZwR/VBXZk2c43bYL46HVo8sKI6fbqIWeurajbj2lS6Oca\nGpt4Y8tBlqzczQc7S4n0CF/MHsL8aSOZPjpVRy6Fuk0v2BIZCJy+CGISoLHeNtU01rd+v6mhxfZ2\n9m2sh5pSO9TTn3ggPsP5oG/+sB/hJIDh9v7A1NDv3K0uhvXPwtqnoHSHbbqaMNdePYyYFvrxB4Am\nBeWz69ARnl21h78W7KW8poHoSA8ThyVx0qgU30+aVnkNPf4lMtriiYKIaNvMFBHdzv2W26LsLO7m\nD/rmBJBwnPtNVr3JGNi70umcftEWNhw01umcng/x6W5HGDSaFNTn1DY08s5HJazZXU5BYRmb9lVS\n32hnUmcNiuOkUSnkOUni+PR4PNoX4b6mRttZ64ls/cO9H37j7ba6KlvaZO1TULTK/k7HXQBf+CFk\nTHA7uoDTpKA6VNvQyKZ9FRTsLqegsJy1e8opO1IPQNKAqGOuJCYNT2ZAtDY5qT6iZDusfdL+1FXC\n+ItsM92QHLcjCxhNCqrLjDHsOnSEgt3lrN1dTsHucnYW28lEkR4hZ2giJ41KJS/TJooh2nGtwt3R\ncvjPH+DDB22F3OyLbXLogwULNSmoXlF+pJ7/7rVXEgW7y1m/9zB1XtvkNDxlgK+5acKwJAbFx5AW\nH82AqAhdV0KFl5oy+M9iWPmQLViYM9cmh/SxbkfWazQpqICo9zaxZX8lBYVlrN1jk0Vx1bHlqmIi\nPaTGRZMyMNrexkWT5nscRUpcNKkDo0mNt7fJA6OJjtSZ2CoE1JTBvx+AlQ/bZVcnfAlm/qhPzHfQ\npKCCwhhDUflRth2oovxIPWU19ZQfqaf0SP0xj8uO1FNZ623zOAkxkTZZOD/NCWTMkATOGj+E1DgX\nxr2r/utIqV3/YtUfwXsUJsyzyWHQCW5H1m2aFFTIaWhsorymnvIjDZQdqae8xiaL5p/mx837lB6p\no7ahCY/A9Kw0zskZwjkTMjguSUt4qCA5cgg++B2sftRO4su9DGb+v2NX7wsTmhRU2DPGsPnTSl7f\ndIDXNx/wdXpPGpHM7JwMzskZwuj0/j1LVQVJdQl8cB+s/pOd+Dfxcpj5Q1uQL0xoUlB9zs7iapZv\nPsDyzQfYUFQBwIlD4m2CmJBB9nGJ2sGtAqvqoL1yKPiTnRk+aT584QeQmuV2ZB3SpKD6tH2Hj/LP\nzQd4fdMBVheW0WRgROoAzsnOYPaEDKaOTNHJdypwqg7A+/dBwWNgGp3k8MPer1PV2GDLq9cetrfJ\nIyAho1uH0qSg+o3S6jre3HqQ1zcd4IOdpdQ3NpGeEMPZ2UM4JyeDU45P03UmVGBU7rfNSgWP2+Qw\n+Up75ZA80j5vjO2L8P9grz0MtRWd2FZhy3L4O/8eyL+2W6FqUlD9UlVtAyu2l7B80wFWbC+mpr6R\nxNhIzhpvO6m/MCZdZ2ar3lf5Kbx/L6x5wiaC1KzPPuAb69t/bUyiXV40NhkGJLe432LbkBxbo6ob\nNCmofq+2oZH3dhxi+eYDvLn1IIdrGoiN8jDzxHRGpAwkMsJDVIQQ4RGiIjxEeuTYbR4PkRF2W6RH\niGzer8VroiI8RHiEgdERjEgZqM1W/VnFPjsJrnLfZx/qsUmtf8APSLEJISIyKKFpUlDKT0NjE6t2\nlbF88wHe2lpMeU093iaDt7GJpl78E0gaEEXeqBTys1LJz0wld1iSTsxTIUGTglKd1NRkaGhqorHJ\n0NBoE4W3yfiSRkOjwdvUhLfx2G2Nzuu8zmsqaxv4757DrCos45MS2xYcE+lh8ohkpjlJYuqoFOJj\ngvPNUCl/nU0K+r9T9XsejxDj6Z1+hsvzbQfjoeo6CgrLWLWrnNWFZSxesZMmAxEeIfu4RPIzU5mW\nlUJeZiqDdC0LFUL0SkGpIKiu89rKs4VlrCos4797PissOHpQHPmZqeRnpTItM5URqQO6Pd+iqclw\n+GgDh6rrOFRVx6Ej9fa2uo7S6nq73dmWEBvJxOFJTByezMThSYzNSNAV+fowbT5SKoTVe5vYuK+C\n1YVlrN5VxurCMl9tqCGJMTZJOD+j0+Mor6mntLqeEufDvtT5YC89Yj/oS5z7ZUfqaWylkyTCI6TF\nRfsq2Q6Kj6HsSD0big5TXtMAQHSEh3HHJZA7LMmXLMYMjidSh/P2CZoUlAojTU2Gj4qrWF1Y7ksS\n+ytq231NbJTH+ZCPIT3+2A/85vvpzv2kAVGtjopqLmi4cV8F64sOs7Gogo1FFVTVeX3nyD4u0Xc1\nMXF4EqMH6ap84UiTglJhrPnDenVhGUXlR0mLjyYtLob0hGhfIoiLDsy6FU1NhsLSI2zcV8GGogo2\nFB1m075KjjY0AhAfE0nO0MRjmp5Gpg7UEiMhTpOCUqrXNDYZPi6pZv3ew75ksWV/JfVOv0jSgCgm\nDk9iwjCbIAYnxJCeEMPghFgGxUdrE1QI0KSglAqoem8THx2scpLEYTYUVbD9QBXeFn0aIpA6MNom\nicRY0uNjGJwY47sdnBDrJJAY4nS4bsDokFSlVEBFR3qYMMxeHcyfZofi1nubOFRdR3FVHcWVtZRU\n11FcaR+XVNVRUlXLjoNVlFTVfS55AAyMjmBwwmeJwiaSGFIHRuNtMtR7m6jzNlHvbaK+sdHeepuo\nb7Tbfc/5bW++X+dtPGbfem8Txtj3ER3pITrCQ0yUvW3eFhPpIToywj7ne9xy/4gW+9vbqAj7Ex1p\nZ8lHRxy7LTrCQ1SkHPvYmR3vJk0KSqleEx3pYWjyAIYmt78QUvPQ2eKqWkqqjk0cxVW1FFfVsXV/\nJe98VEd1Xesr9kV45JgP8OYPbv8P6NgoD4mxkc62CN/+zfuJQIPXHJMw6lskl4qjDdQ1NB6TYOob\nm6hrsLetjfbqCY/gJJLPEkVUpH2v3znrRC6aNLRXz9eSJgWlVNB5POJbenVcB5Wga+q9lNc0EOUR\nYiI/+1bu9jfqZo2+K5hGX0JpaLQJo8Fr7K3zU+97ztDgJJfPthvf877X+z/X2ETKwKiAvx9NCkqp\nkDYwOpKB0aH7URXhEQZER/SZ6rs6JEAppZSPJgWllFI+mhSUUkr5BDQpiMhsEdkuIjtFZFErz8eI\nyF+c51eKSGYg41FKKdW+gCUFEYkAFgPnAtnAfBHJbrHbtUC5MeYE4F7g14GKRymlVMcCeaUwDdhp\njPnEGFMPLAXmtNhnDvBn5/7zwJmiBVSUUso1gUwKw4C9fo+LnG2t7mOM8QIVQFrLA4nI9SJSICIF\nJSUlAQpXKaVUWHQ0G2MeMcbkGWPy0tPT3Q5HKaX6rEDOCNkHjPB7PNzZ1to+RSISCSQBpe0ddM2a\nNYdEZHdvBtoLBgGH3A6iC8IpXo01cMIp3nCKFUIz3lGd2SmQSWE1MEZEsrAf/lcAC1rs8zLwFeA/\nwDzgbdNB2VZjTMhdKohIQWeqD4aKcIpXYw2ccIo3nGKF8IvXX8CSgjHGKyI3AsuBCOAxY8xmEbkD\nKDDGvAz8CXhKRHYCZdjEoZRSyiUBLShijFkGLGux7Ta/+7XApYGMQSmlVOeFRUdzGHjE7QC6KJzi\n1VgDJ5ziDadYIfzi9Qm7ldeUUkoFjl4pKKWU8tGkoJRSykeTQg+IyAgRWSEiW0Rks4h8x+2YOiIi\nESLyXxH5h9uxdEREkkXkeRHZJiJbReQUt2Nqi4h8z/k/sElEnhWRWLdj8icij4lIsYhs8tuWKiJv\niMgO5zbFzRibtRHrb53/BxtE5EURSXYzRn+txev33PdFxIjIIDdi6w5NCj3jBb5vjMkGTga+1UrR\nv1DzHWCr20F00u+A140x44BJhGjcIjIMuAnIM8ZMwA7BDrXh1U8As1tsWwS8ZYwZA7zlPA4FT/D5\nWN8AJhhjJgIfAT8OdlDteILPx4uIjADOBvYEO6Ce0KTQA8aY/caYtc79KuyHVsv6TiFDRIYD5wOP\nuh1LR0QkCfgCdi4Lxph6Y8xhd6NqVyQwwJmZPxD41OV4jmGMeRc7F8iff0HKPwMXBzWoNrQWqzHm\nn059NIAPsRUSQkIbv1uwlZ//HxBWo3k0KfQSZy2IKcBKdyNp133Y/6RNbgfSCVlACfC409z1qIjE\nuR1Ua4wx+4C7sd8I9wMVxph/uhtVpwwxxux37h8AhrgZTBd8FXjN7SDaIyJzgH3GmPVux9JVmhR6\ngYjEAy8A3zXGVLodT2tE5AKg2Bizxu1YOikSmAo8aIyZAhwhdJo3juG0xc/BJrKhQJyILHQ3qq5x\nysuE/DdaEfkJttl2iduxtEVEBgK3ALd1tG8o0qTQQyIShU0IS4wxf3M7nnbMAC4SkULs2hZniMjT\n7obUriKgyBjTfOX1PDZJhKKzgF3GmBJjTAPwN+B/XI6pMw6KyHEAzm2xy/G0S0SuBi4AruyoRprL\njsd+QVjv/L0NB9aKSIarUXWSJoUecBYE+hOw1Rhzj9vxtMcY82NjzHBjTCa2E/RtY0zIfps1xhwA\n9orIWGfTmcAWF0Nqzx7gZBEZ6PyfOJMQ7RRvobkgJc7tSy7G0i4RmY1t+rzIGFPjdjztMcZsNMYM\nNsZkOn9vRcBU5/90yNOk0DMzgKuw37rXOT/nuR1UH/JtYImIbAAmA790OZ5WOVczzwNrgY3Yv6uQ\nKnMgIs9iqxGPFZEiEbkWuAv4oojswF7t3OVmjM3aiPX3QALwhvN39pCrQfppI96wpWUulFJK+eiV\nglJKKR9NCkoppXw0KSillPLRpKCUUspHk4JSSikfTQpKBZiInB4OVWmVAk0KSiml/GhSUMohIgtF\nZJUzOephZ+2JahG511kr4S0RSXf2nSwiH/rV909xtp8gIm+KyHoRWSsixzuHj/dbG2KJM/MZEbnL\nWY9jg4jc7dJbV8pHk4JSgIiMBy4HZhhjJgONwJVAHFBgjMkB3gF+6rzkSeBHTn3/jX7blwCLjTGT\nsPWPmquQTgG+C2QDo4EZIpIGzAVynOP8IrDvUqmOaVJQyjoTOAlYLSLrnMejsWXG/+Ls8zRwqrPW\nQ7Ix5h1n+5+BL4hIAjDMGPMigDGm1q9OzypjTJExpglYB2QCFUAt8CcRuQQI6Zo+qn/QpKCUJcCf\njTGTnZ+xxpjbW9mvu3Vh6vzuNwKRzqIx07B1ky4AXu/msZXqNZoUlLLeAuaJyGDwrV88Cvs3Ms/Z\nZwHwvjGmAigXkdOc7VcB7zir7xWJyMXOMWKc2vqtctbhSDLGLAO+h11yVClXRbodgFKhwBizRURu\nBf4pIh6gAfgWdnGfac5zxdh+B7Clph9yPvQ/Aa5xtl8FPCwidzjHuLSd0yYAL4lILPZK5eZefltK\ndZlWSVWqHSJSbYyJdzsOpYJFm4+UUkr56JWCUkopH71SUEop5aNJQSmllI8mBaWUUj6aFJRSSvlo\nUlBKKeXz/wFaSpw9jV6k+QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oE3qgfE8T3vw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.save('new_helmet_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9HCIf-kUYAH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}