Car Brand Classifier!

├── Breast cancer Detection ├── Breast Cancer.ipynb ├── Readme.md └── Screenshot.png ├── Car Brand Classifier And Deployment ├── Procfile ├── README.md ├── Transfer Learning Resnet 50.ipynb ├── app.py ├── model-Resnet-50-h5 (Download Link).txt ├── requirements.txt ├── static │ ├── css │ │ └── main.css │ └── js │ │ └── main.js └── templates │ ├── base.html │ └── index.html ├── Cat Dog Images Classifier (CNN + Keras) ├── Cat_Dog_Classifier_Using_CNN.ipynb ├── Cat_Dog_Classifier_Using_CNN.py ├── DataSet │ └── How to Download Dataset....txt ├── Image.gif └── Readme.md ├── Covid19 FaceMask Detector (CNN & OpenCV) ├── Readme.md ├── face_mask_detection.ipynb ├── haarcascade_frontalface_default.xml ├── man-mask-protective.jpg ├── mask.py ├── video.mp4 ├── video1.mp4 ├── video2.mp4 └── women with mask.jpg ├── Digit Recognizer Kaggle ├── My_Submission.csv ├── Readme.md └── digit-recognizer-detailed-step-wise.ipynb ├── Image Classifier Using Resnet50 ├── README.md ├── Screenshot1.PNG ├── Screenshot2.PNG ├── image-classifier-using-resnet50.ipynb └── images │ ├── Image1.jpg │ ├── Image3.jpg │ ├── Scooter.jpg │ ├── banana.jpg │ ├── car.jpg │ ├── image10.jpg │ ├── image11.jpg │ ├── image2.jpg │ ├── image4.jpg │ ├── image6.jpg │ ├── image8.jpg │ └── image9.jpg ├── Keras Introduction Exploration ├── A Gentle Introduction to Keras.ipynb ├── BBC.csv ├── BBCN.csv ├── Readme.md └── Screenshot.jpeg └── Readme.md /Breast cancer Detection/Readme.md: -------------------------------------------------------------------------------- 1 | # Breast Cancer Prediction 2 | > Predicts whether the type of breast cancer is Malignant or Benign 3 | 4 | ## Focus of the Project 5 | #### > To predict if a breast cancer is Malignant or Benign using Image Dataset as well as Numerical Data 6 | #### > Apply ML and DL Models to predict the severity of the Breast-Cancer 7 |

8 | 9 | 10 | ## About Project: 11 | 12 | Breast cancer is the most common type of cancer in women. When cancers are found early, they can often be cured. 13 | There are some devices that detect the breast cancer but many times they lead to false positives, which results 14 | is patients undergoing painful, expensive surgeries that were not even necessary. These type of cancers are called 15 | **benign** which do not require surgeries and we can reduce these unnecessary surgeries by using Machine Learning. 16 | I have taken the dataset of the previous breast cancer patients and train the model to predict whether the cancer is **benign** or **malignant**. These predictions will help doctors to do surgeries only when the cancer is malignant, thus reducing the unnecessary surgeries for woman. 17 | 18 | For building the project I have used Wisconsin Breast cancer data which has 569 rows of which 357 are benign and 212 are malignant. 19 | The data is prepossessed and scaled. I have trained with Random forest Classifier gives best accuracy of 95.0%. 20 | 21 | ## Languages Used: 22 | 23 | * Python: language 24 | * NumPy: library for numerical calculations 25 | * Pandas: library for data manipulation and analysis 26 | * SkLearn: library which features various classification, regression and clustering algorithms 27 | 28 | Thank You! 29 | 30 | #### Feel Free to contact me at➛ amark720@gmail.com for any help related to this Project! 31 | -------------------------------------------------------------------------------- /Breast cancer Detection/Screenshot.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/amark720/Deep-Learning-Projects/0ae283356592451eb834af43494545324eead24c/Breast cancer Detection/Screenshot.png -------------------------------------------------------------------------------- /Car Brand Classifier And Deployment/Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn app:main -------------------------------------------------------------------------------- /Car Brand Classifier And Deployment/README.md: -------------------------------------------------------------------------------- 1 | # (Deep Learning) Car-Brand-Classifier 2 | "Car Brand Classifier App" Classifies cars of different brands using transfer learning technique Resnet-50. Here Transfer learning recognizes car brands of 3 different brands(i.e. Audi/Lamborghini/Mercedes). For the training set we used 80 images and for validation set 52 images. 3 | 4 |
5 | 6 | ### ScreenRecording Clip of Live App. 7 | [![Demo Doccou alpha](https://github.com/amark720/Amar-kumar/blob/master/ScreenShots/Car%20Brand%20Classifier%20GIF.gif)](http://ec2-18-220-203-245.us-east-2.compute.amazonaws.com:8080) 8 | 9 | ### Web App and Deployment 10 | 11 | This project uses Flask for the web app and its deployment is done on AWS Ec2. 12 | 13 | ## Live Project Link: http://ec2-18-220-203-245.us-east-2.compute.amazonaws.com:8080 14 | 15 | 16 | 17 | ### ScreenShots: 18 | 19 | #### Landing Page- 20 | 21 |

22 | 23 | #### Result- 24 | 25 |

26 | 27 | #### Improvements 28 | * Here we've used a very less amount of images to train the model. So, the Model can be improved further by adding more images to the training set. 29 | * We can add more Classes of Images to help predecting Cars of many Brands. 30 | * Adding more Layers and Epocs into Neural Network will further improve the Accuracy. 31 | 32 | 33 | #### Feel Free to contact me at➛ amark720@gmail.com for any help related to this Project! 34 | 40 | -------------------------------------------------------------------------------- /Car Brand Classifier And Deployment/Transfer Learning Resnet 50.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Transfer Learning VGG 16 and VGG 19 using Keras" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "Please download the dataset from the below url" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": 67, 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "# import the libraries as shown below\n", 24 | "\n", 25 | "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n", 26 | "from tensorflow.keras.models import Model\n", 27 | "from tensorflow.keras.applications.resnet50 import ResNet50\n", 28 | "#from keras.applications.vgg16 import VGG16\n", 29 | "from tensorflow.keras.applications.resnet50 import preprocess_input\n", 30 | "from tensorflow.keras.preprocessing import image\n", 31 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n", 32 | "from tensorflow.keras.models import Sequential\n", 33 | "import numpy as np\n", 34 | "from glob import glob\n", 35 | "import matplotlib.pyplot as plt" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 68, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# re-size all the images to this\n", 45 | "IMAGE_SIZE = [224, 224]\n", 46 | "\n", 47 | "train_path = 'Datasets/train'\n", 48 | "valid_path = 'Datasets/test'\n" 49 | ] 50 | }, 51 | { 52 | "cell_type": "code", 53 | "execution_count": 69, 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "# Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG\n", 58 | "# Here we will be using imagenet weights\n", 59 | "\n", 60 | "resnet = ResNet50(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n", 61 | "\n", 62 | "\n" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 70, 68 | "metadata": {}, 69 | "outputs": [], 70 | "source": [ 71 | "# don't train existing weights\n", 72 | "for layer in resnet.layers:\n", 73 | " layer.trainable = False" 74 | ] 75 | }, 76 | { 77 | "cell_type": "code", 78 | "execution_count": 71, 79 | "metadata": {}, 80 | "outputs": [ 81 | { 82 | "data": { 83 | "text/plain": [ 84 | "['Datasets/train\\\\audi',\n", 85 | " 'Datasets/train\\\\lamborghini',\n", 86 | " 'Datasets/train\\\\mercedes']" 87 | ] 88 | }, 89 | "execution_count": 71, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | } 93 | ], 94 | "source": [ 95 | " # useful for getting number of output classes\n", 96 | "folders = glob('Datasets/train/*')\n", 97 | "folders" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 72, 103 | "metadata": {}, 104 | "outputs": [], 105 | "source": [ 106 | "# our layers - you can add more if you want\n", 107 | "x = Flatten()(resnet.output)" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": 73, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [ 116 | "prediction = Dense(len(folders), activation='softmax')(x)\n", 117 | "\n", 118 | "# create a model object\n", 119 | "model = Model(inputs=resnet.input, outputs=prediction)" 120 | ] 121 | }, 122 | { 123 | "cell_type": "code", 124 | "execution_count": 74, 125 | "metadata": {}, 126 | "outputs": [ 127 | { 128 | "name": "stdout", 129 | "output_type": "stream", 130 | "text": [ 131 | "Model: \"model_2\"\n", 132 | "__________________________________________________________________________________________________\n", 133 | "Layer (type) Output Shape Param # Connected to \n", 134 | "==================================================================================================\n", 135 | "input_3 (InputLayer) [(None, 224, 224, 3) 0 \n", 136 | "__________________________________________________________________________________________________\n", 137 | "conv1_pad (ZeroPadding2D) (None, 230, 230, 3) 0 input_3[0][0] \n", 138 | "__________________________________________________________________________________________________\n", 139 | "conv1_conv (Conv2D) (None, 112, 112, 64) 9472 conv1_pad[0][0] \n", 140 | "__________________________________________________________________________________________________\n", 141 | "conv1_bn (BatchNormalization) (None, 112, 112, 64) 256 conv1_conv[0][0] \n", 142 | "__________________________________________________________________________________________________\n", 143 | "conv1_relu (Activation) (None, 112, 112, 64) 0 conv1_bn[0][0] \n", 144 | "__________________________________________________________________________________________________\n", 145 | "pool1_pad (ZeroPadding2D) (None, 114, 114, 64) 0 conv1_relu[0][0] \n", 146 | "__________________________________________________________________________________________________\n", 147 | "pool1_pool (MaxPooling2D) (None, 56, 56, 64) 0 pool1_pad[0][0] \n", 148 | "__________________________________________________________________________________________________\n", 149 | "conv2_block1_1_conv (Conv2D) (None, 56, 56, 64) 4160 pool1_pool[0][0] \n", 150 | "__________________________________________________________________________________________________\n", 151 | "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64) 256 conv2_block1_1_conv[0][0] \n", 152 | "__________________________________________________________________________________________________\n", 153 | "conv2_block1_1_relu (Activation (None, 56, 56, 64) 0 conv2_block1_1_bn[0][0] \n", 154 | "__________________________________________________________________________________________________\n", 155 | "conv2_block1_2_conv (Conv2D) (None, 56, 56, 64) 36928 conv2_block1_1_relu[0][0] \n", 156 | "__________________________________________________________________________________________________\n", 157 | "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64) 256 conv2_block1_2_conv[0][0] \n", 158 | "__________________________________________________________________________________________________\n", 159 | "conv2_block1_2_relu (Activation (None, 56, 56, 64) 0 conv2_block1_2_bn[0][0] \n", 160 | "__________________________________________________________________________________________________\n", 161 | "conv2_block1_0_conv (Conv2D) (None, 56, 56, 256) 16640 pool1_pool[0][0] \n", 162 | "__________________________________________________________________________________________________\n", 163 | "conv2_block1_3_conv (Conv2D) (None, 56, 56, 256) 16640 conv2_block1_2_relu[0][0] \n", 164 | "__________________________________________________________________________________________________\n", 165 | "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256) 1024 conv2_block1_0_conv[0][0] \n", 166 | "__________________________________________________________________________________________________\n", 167 | "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256) 1024 conv2_block1_3_conv[0][0] \n", 168 | "__________________________________________________________________________________________________\n", 169 | "conv2_block1_add (Add) (None, 56, 56, 256) 0 conv2_block1_0_bn[0][0] \n", 170 | " conv2_block1_3_bn[0][0] \n", 171 | "__________________________________________________________________________________________________\n", 172 | "conv2_block1_out (Activation) (None, 56, 56, 256) 0 conv2_block1_add[0][0] \n", 173 | "__________________________________________________________________________________________________\n", 174 | "conv2_block2_1_conv (Conv2D) (None, 56, 56, 64) 16448 conv2_block1_out[0][0] \n", 175 | "__________________________________________________________________________________________________\n", 176 | "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64) 256 conv2_block2_1_conv[0][0] \n", 177 | "__________________________________________________________________________________________________\n", 178 | "conv2_block2_1_relu (Activation (None, 56, 56, 64) 0 conv2_block2_1_bn[0][0] \n", 179 | "__________________________________________________________________________________________________\n", 180 | "conv2_block2_2_conv (Conv2D) (None, 56, 56, 64) 36928 conv2_block2_1_relu[0][0] \n", 181 | "__________________________________________________________________________________________________\n", 182 | "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64) 256 conv2_block2_2_conv[0][0] \n", 183 | "__________________________________________________________________________________________________\n", 184 | "conv2_block2_2_relu (Activation (None, 56, 56, 64) 0 conv2_block2_2_bn[0][0] \n", 185 | "__________________________________________________________________________________________________\n", 186 | "conv2_block2_3_conv (Conv2D) (None, 56, 56, 256) 16640 conv2_block2_2_relu[0][0] \n", 187 | "__________________________________________________________________________________________________\n", 188 | "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256) 1024 conv2_block2_3_conv[0][0] \n", 189 | "__________________________________________________________________________________________________\n", 190 | "conv2_block2_add (Add) (None, 56, 56, 256) 0 conv2_block1_out[0][0] \n", 191 | " conv2_block2_3_bn[0][0] \n", 192 | "__________________________________________________________________________________________________\n", 193 | "conv2_block2_out (Activation) (None, 56, 56, 256) 0 conv2_block2_add[0][0] \n", 194 | "__________________________________________________________________________________________________\n", 195 | "conv2_block3_1_conv (Conv2D) (None, 56, 56, 64) 16448 conv2_block2_out[0][0] \n", 196 | "__________________________________________________________________________________________________\n", 197 | "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64) 256 conv2_block3_1_conv[0][0] \n", 198 | "__________________________________________________________________________________________________\n", 199 | "conv2_block3_1_relu (Activation (None, 56, 56, 64) 0 conv2_block3_1_bn[0][0] \n", 200 | "__________________________________________________________________________________________________\n", 201 | "conv2_block3_2_conv (Conv2D) (None, 56, 56, 64) 36928 conv2_block3_1_relu[0][0] \n", 202 | "__________________________________________________________________________________________________\n", 203 | "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64) 256 conv2_block3_2_conv[0][0] \n", 204 | "__________________________________________________________________________________________________\n", 205 | "conv2_block3_2_relu (Activation (None, 56, 56, 64) 0 conv2_block3_2_bn[0][0] \n", 206 | "__________________________________________________________________________________________________\n", 207 | "conv2_block3_3_conv (Conv2D) (None, 56, 56, 256) 16640 conv2_block3_2_relu[0][0] \n", 208 | "__________________________________________________________________________________________________\n", 209 | "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256) 1024 conv2_block3_3_conv[0][0] \n", 210 | "__________________________________________________________________________________________________\n", 211 | "conv2_block3_add (Add) (None, 56, 56, 256) 0 conv2_block2_out[0][0] \n", 212 | " conv2_block3_3_bn[0][0] \n", 213 | "__________________________________________________________________________________________________\n", 214 | "conv2_block3_out (Activation) (None, 56, 56, 256) 0 conv2_block3_add[0][0] \n", 215 | "__________________________________________________________________________________________________\n", 216 | "conv3_block1_1_conv (Conv2D) (None, 28, 28, 128) 32896 conv2_block3_out[0][0] \n", 217 | "__________________________________________________________________________________________________\n", 218 | "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block1_1_conv[0][0] \n", 219 | "__________________________________________________________________________________________________\n", 220 | "conv3_block1_1_relu (Activation (None, 28, 28, 128) 0 conv3_block1_1_bn[0][0] \n", 221 | "__________________________________________________________________________________________________\n", 222 | "conv3_block1_2_conv (Conv2D) (None, 28, 28, 128) 147584 conv3_block1_1_relu[0][0] \n", 223 | "__________________________________________________________________________________________________\n", 224 | "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block1_2_conv[0][0] \n", 225 | "__________________________________________________________________________________________________\n", 226 | "conv3_block1_2_relu (Activation (None, 28, 28, 128) 0 conv3_block1_2_bn[0][0] \n", 227 | "__________________________________________________________________________________________________\n", 228 | "conv3_block1_0_conv (Conv2D) (None, 28, 28, 512) 131584 conv2_block3_out[0][0] \n", 229 | "__________________________________________________________________________________________________\n", 230 | "conv3_block1_3_conv (Conv2D) (None, 28, 28, 512) 66048 conv3_block1_2_relu[0][0] \n", 231 | "__________________________________________________________________________________________________\n", 232 | "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512) 2048 conv3_block1_0_conv[0][0] \n", 233 | "__________________________________________________________________________________________________\n", 234 | "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512) 2048 conv3_block1_3_conv[0][0] \n", 235 | "__________________________________________________________________________________________________\n", 236 | "conv3_block1_add (Add) (None, 28, 28, 512) 0 conv3_block1_0_bn[0][0] \n", 237 | " conv3_block1_3_bn[0][0] \n", 238 | "__________________________________________________________________________________________________\n", 239 | "conv3_block1_out (Activation) (None, 28, 28, 512) 0 conv3_block1_add[0][0] \n", 240 | "__________________________________________________________________________________________________\n", 241 | "conv3_block2_1_conv (Conv2D) (None, 28, 28, 128) 65664 conv3_block1_out[0][0] \n", 242 | "__________________________________________________________________________________________________\n", 243 | "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block2_1_conv[0][0] \n", 244 | "__________________________________________________________________________________________________\n", 245 | "conv3_block2_1_relu (Activation (None, 28, 28, 128) 0 conv3_block2_1_bn[0][0] \n", 246 | "__________________________________________________________________________________________________\n", 247 | "conv3_block2_2_conv (Conv2D) (None, 28, 28, 128) 147584 conv3_block2_1_relu[0][0] \n", 248 | "__________________________________________________________________________________________________\n", 249 | "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block2_2_conv[0][0] \n", 250 | "__________________________________________________________________________________________________\n", 251 | "conv3_block2_2_relu (Activation (None, 28, 28, 128) 0 conv3_block2_2_bn[0][0] \n", 252 | "__________________________________________________________________________________________________\n", 253 | "conv3_block2_3_conv (Conv2D) (None, 28, 28, 512) 66048 conv3_block2_2_relu[0][0] \n", 254 | "__________________________________________________________________________________________________\n", 255 | "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512) 2048 conv3_block2_3_conv[0][0] \n", 256 | "__________________________________________________________________________________________________\n", 257 | "conv3_block2_add (Add) (None, 28, 28, 512) 0 conv3_block1_out[0][0] \n", 258 | " conv3_block2_3_bn[0][0] \n", 259 | "__________________________________________________________________________________________________\n", 260 | "conv3_block2_out (Activation) (None, 28, 28, 512) 0 conv3_block2_add[0][0] \n", 261 | "__________________________________________________________________________________________________\n", 262 | "conv3_block3_1_conv (Conv2D) (None, 28, 28, 128) 65664 conv3_block2_out[0][0] \n", 263 | "__________________________________________________________________________________________________\n", 264 | "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block3_1_conv[0][0] \n", 265 | "__________________________________________________________________________________________________\n", 266 | "conv3_block3_1_relu (Activation (None, 28, 28, 128) 0 conv3_block3_1_bn[0][0] \n", 267 | "__________________________________________________________________________________________________\n", 268 | "conv3_block3_2_conv (Conv2D) (None, 28, 28, 128) 147584 conv3_block3_1_relu[0][0] \n", 269 | "__________________________________________________________________________________________________\n", 270 | "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block3_2_conv[0][0] \n", 271 | "__________________________________________________________________________________________________\n", 272 | "conv3_block3_2_relu (Activation (None, 28, 28, 128) 0 conv3_block3_2_bn[0][0] \n", 273 | "__________________________________________________________________________________________________\n", 274 | "conv3_block3_3_conv (Conv2D) (None, 28, 28, 512) 66048 conv3_block3_2_relu[0][0] \n", 275 | "__________________________________________________________________________________________________\n", 276 | "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512) 2048 conv3_block3_3_conv[0][0] \n", 277 | "__________________________________________________________________________________________________\n", 278 | "conv3_block3_add (Add) (None, 28, 28, 512) 0 conv3_block2_out[0][0] \n", 279 | " conv3_block3_3_bn[0][0] \n", 280 | "__________________________________________________________________________________________________\n", 281 | "conv3_block3_out (Activation) (None, 28, 28, 512) 0 conv3_block3_add[0][0] \n", 282 | "__________________________________________________________________________________________________\n", 283 | "conv3_block4_1_conv (Conv2D) (None, 28, 28, 128) 65664 conv3_block3_out[0][0] \n", 284 | "__________________________________________________________________________________________________\n", 285 | "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block4_1_conv[0][0] \n", 286 | "__________________________________________________________________________________________________\n", 287 | "conv3_block4_1_relu (Activation (None, 28, 28, 128) 0 conv3_block4_1_bn[0][0] \n", 288 | "__________________________________________________________________________________________________\n", 289 | "conv3_block4_2_conv (Conv2D) (None, 28, 28, 128) 147584 conv3_block4_1_relu[0][0] \n", 290 | "__________________________________________________________________________________________________\n", 291 | "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128) 512 conv3_block4_2_conv[0][0] \n", 292 | "__________________________________________________________________________________________________\n", 293 | "conv3_block4_2_relu (Activation (None, 28, 28, 128) 0 conv3_block4_2_bn[0][0] \n", 294 | "__________________________________________________________________________________________________\n", 295 | "conv3_block4_3_conv (Conv2D) (None, 28, 28, 512) 66048 conv3_block4_2_relu[0][0] \n", 296 | "__________________________________________________________________________________________________\n", 297 | "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512) 2048 conv3_block4_3_conv[0][0] \n", 298 | "__________________________________________________________________________________________________\n", 299 | "conv3_block4_add (Add) (None, 28, 28, 512) 0 conv3_block3_out[0][0] \n", 300 | " conv3_block4_3_bn[0][0] \n", 301 | "__________________________________________________________________________________________________\n", 302 | "conv3_block4_out (Activation) (None, 28, 28, 512) 0 conv3_block4_add[0][0] \n", 303 | "__________________________________________________________________________________________________\n", 304 | "conv4_block1_1_conv (Conv2D) (None, 14, 14, 256) 131328 conv3_block4_out[0][0] \n", 305 | "__________________________________________________________________________________________________\n", 306 | "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block1_1_conv[0][0] \n", 307 | "__________________________________________________________________________________________________\n", 308 | "conv4_block1_1_relu (Activation (None, 14, 14, 256) 0 conv4_block1_1_bn[0][0] \n", 309 | "__________________________________________________________________________________________________\n", 310 | "conv4_block1_2_conv (Conv2D) (None, 14, 14, 256) 590080 conv4_block1_1_relu[0][0] \n", 311 | "__________________________________________________________________________________________________\n", 312 | "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block1_2_conv[0][0] \n", 313 | "__________________________________________________________________________________________________\n", 314 | "conv4_block1_2_relu (Activation (None, 14, 14, 256) 0 conv4_block1_2_bn[0][0] \n", 315 | "__________________________________________________________________________________________________\n", 316 | "conv4_block1_0_conv (Conv2D) (None, 14, 14, 1024) 525312 conv3_block4_out[0][0] \n", 317 | "__________________________________________________________________________________________________\n", 318 | "conv4_block1_3_conv (Conv2D) (None, 14, 14, 1024) 263168 conv4_block1_2_relu[0][0] \n", 319 | "__________________________________________________________________________________________________\n", 320 | "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block1_0_conv[0][0] \n", 321 | "__________________________________________________________________________________________________\n", 322 | "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block1_3_conv[0][0] \n", 323 | "__________________________________________________________________________________________________\n", 324 | "conv4_block1_add (Add) (None, 14, 14, 1024) 0 conv4_block1_0_bn[0][0] \n", 325 | " conv4_block1_3_bn[0][0] \n", 326 | "__________________________________________________________________________________________________\n", 327 | "conv4_block1_out (Activation) (None, 14, 14, 1024) 0 conv4_block1_add[0][0] \n", 328 | "__________________________________________________________________________________________________\n", 329 | "conv4_block2_1_conv (Conv2D) (None, 14, 14, 256) 262400 conv4_block1_out[0][0] \n", 330 | "__________________________________________________________________________________________________\n", 331 | "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block2_1_conv[0][0] \n", 332 | "__________________________________________________________________________________________________\n", 333 | "conv4_block2_1_relu (Activation (None, 14, 14, 256) 0 conv4_block2_1_bn[0][0] \n", 334 | "__________________________________________________________________________________________________\n", 335 | "conv4_block2_2_conv (Conv2D) (None, 14, 14, 256) 590080 conv4_block2_1_relu[0][0] \n", 336 | "__________________________________________________________________________________________________\n", 337 | "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block2_2_conv[0][0] \n", 338 | "__________________________________________________________________________________________________\n", 339 | "conv4_block2_2_relu (Activation (None, 14, 14, 256) 0 conv4_block2_2_bn[0][0] \n", 340 | "__________________________________________________________________________________________________\n", 341 | "conv4_block2_3_conv (Conv2D) (None, 14, 14, 1024) 263168 conv4_block2_2_relu[0][0] \n", 342 | "__________________________________________________________________________________________________\n", 343 | "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block2_3_conv[0][0] \n", 344 | "__________________________________________________________________________________________________\n", 345 | "conv4_block2_add (Add) (None, 14, 14, 1024) 0 conv4_block1_out[0][0] \n", 346 | " conv4_block2_3_bn[0][0] \n", 347 | "__________________________________________________________________________________________________\n", 348 | "conv4_block2_out (Activation) (None, 14, 14, 1024) 0 conv4_block2_add[0][0] \n", 349 | "__________________________________________________________________________________________________\n", 350 | "conv4_block3_1_conv (Conv2D) (None, 14, 14, 256) 262400 conv4_block2_out[0][0] \n", 351 | "__________________________________________________________________________________________________\n", 352 | "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block3_1_conv[0][0] \n", 353 | "__________________________________________________________________________________________________\n", 354 | "conv4_block3_1_relu (Activation (None, 14, 14, 256) 0 conv4_block3_1_bn[0][0] \n", 355 | "__________________________________________________________________________________________________\n", 356 | "conv4_block3_2_conv (Conv2D) (None, 14, 14, 256) 590080 conv4_block3_1_relu[0][0] \n", 357 | "__________________________________________________________________________________________________\n", 358 | "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block3_2_conv[0][0] \n", 359 | "__________________________________________________________________________________________________\n", 360 | "conv4_block3_2_relu (Activation (None, 14, 14, 256) 0 conv4_block3_2_bn[0][0] \n", 361 | "__________________________________________________________________________________________________\n", 362 | "conv4_block3_3_conv (Conv2D) (None, 14, 14, 1024) 263168 conv4_block3_2_relu[0][0] \n", 363 | "__________________________________________________________________________________________________\n", 364 | "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block3_3_conv[0][0] \n", 365 | "__________________________________________________________________________________________________\n", 366 | "conv4_block3_add (Add) (None, 14, 14, 1024) 0 conv4_block2_out[0][0] \n", 367 | " conv4_block3_3_bn[0][0] \n", 368 | "__________________________________________________________________________________________________\n", 369 | "conv4_block3_out (Activation) (None, 14, 14, 1024) 0 conv4_block3_add[0][0] \n", 370 | "__________________________________________________________________________________________________\n", 371 | "conv4_block4_1_conv (Conv2D) (None, 14, 14, 256) 262400 conv4_block3_out[0][0] \n", 372 | "__________________________________________________________________________________________________\n", 373 | "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block4_1_conv[0][0] \n", 374 | "__________________________________________________________________________________________________\n", 375 | "conv4_block4_1_relu (Activation (None, 14, 14, 256) 0 conv4_block4_1_bn[0][0] \n", 376 | "__________________________________________________________________________________________________\n", 377 | "conv4_block4_2_conv (Conv2D) (None, 14, 14, 256) 590080 conv4_block4_1_relu[0][0] \n", 378 | "__________________________________________________________________________________________________\n", 379 | "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block4_2_conv[0][0] \n", 380 | "__________________________________________________________________________________________________\n", 381 | "conv4_block4_2_relu (Activation (None, 14, 14, 256) 0 conv4_block4_2_bn[0][0] \n", 382 | "__________________________________________________________________________________________________\n", 383 | "conv4_block4_3_conv (Conv2D) (None, 14, 14, 1024) 263168 conv4_block4_2_relu[0][0] \n", 384 | "__________________________________________________________________________________________________\n", 385 | "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block4_3_conv[0][0] \n", 386 | "__________________________________________________________________________________________________\n", 387 | "conv4_block4_add (Add) (None, 14, 14, 1024) 0 conv4_block3_out[0][0] \n", 388 | " conv4_block4_3_bn[0][0] \n", 389 | "__________________________________________________________________________________________________\n", 390 | "conv4_block4_out (Activation) (None, 14, 14, 1024) 0 conv4_block4_add[0][0] \n", 391 | "__________________________________________________________________________________________________\n", 392 | "conv4_block5_1_conv (Conv2D) (None, 14, 14, 256) 262400 conv4_block4_out[0][0] \n", 393 | "__________________________________________________________________________________________________\n", 394 | "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block5_1_conv[0][0] \n", 395 | "__________________________________________________________________________________________________\n", 396 | "conv4_block5_1_relu (Activation (None, 14, 14, 256) 0 conv4_block5_1_bn[0][0] \n", 397 | "__________________________________________________________________________________________________\n", 398 | "conv4_block5_2_conv (Conv2D) (None, 14, 14, 256) 590080 conv4_block5_1_relu[0][0] \n", 399 | "__________________________________________________________________________________________________\n", 400 | "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block5_2_conv[0][0] \n", 401 | "__________________________________________________________________________________________________\n", 402 | "conv4_block5_2_relu (Activation (None, 14, 14, 256) 0 conv4_block5_2_bn[0][0] \n", 403 | "__________________________________________________________________________________________________\n", 404 | "conv4_block5_3_conv (Conv2D) (None, 14, 14, 1024) 263168 conv4_block5_2_relu[0][0] \n", 405 | "__________________________________________________________________________________________________\n", 406 | "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block5_3_conv[0][0] \n", 407 | "__________________________________________________________________________________________________\n", 408 | "conv4_block5_add (Add) (None, 14, 14, 1024) 0 conv4_block4_out[0][0] \n", 409 | " conv4_block5_3_bn[0][0] \n", 410 | "__________________________________________________________________________________________________\n", 411 | "conv4_block5_out (Activation) (None, 14, 14, 1024) 0 conv4_block5_add[0][0] \n", 412 | "__________________________________________________________________________________________________\n", 413 | "conv4_block6_1_conv (Conv2D) (None, 14, 14, 256) 262400 conv4_block5_out[0][0] \n", 414 | "__________________________________________________________________________________________________\n", 415 | "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block6_1_conv[0][0] \n", 416 | "__________________________________________________________________________________________________\n", 417 | "conv4_block6_1_relu (Activation (None, 14, 14, 256) 0 conv4_block6_1_bn[0][0] \n", 418 | "__________________________________________________________________________________________________\n", 419 | "conv4_block6_2_conv (Conv2D) (None, 14, 14, 256) 590080 conv4_block6_1_relu[0][0] \n", 420 | "__________________________________________________________________________________________________\n", 421 | "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256) 1024 conv4_block6_2_conv[0][0] \n", 422 | "__________________________________________________________________________________________________\n", 423 | "conv4_block6_2_relu (Activation (None, 14, 14, 256) 0 conv4_block6_2_bn[0][0] \n", 424 | "__________________________________________________________________________________________________\n", 425 | "conv4_block6_3_conv (Conv2D) (None, 14, 14, 1024) 263168 conv4_block6_2_relu[0][0] \n", 426 | "__________________________________________________________________________________________________\n", 427 | "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096 conv4_block6_3_conv[0][0] \n", 428 | "__________________________________________________________________________________________________\n", 429 | "conv4_block6_add (Add) (None, 14, 14, 1024) 0 conv4_block5_out[0][0] \n", 430 | " conv4_block6_3_bn[0][0] \n", 431 | "__________________________________________________________________________________________________\n", 432 | "conv4_block6_out (Activation) (None, 14, 14, 1024) 0 conv4_block6_add[0][0] \n", 433 | "__________________________________________________________________________________________________\n", 434 | "conv5_block1_1_conv (Conv2D) (None, 7, 7, 512) 524800 conv4_block6_out[0][0] \n", 435 | "__________________________________________________________________________________________________\n", 436 | "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block1_1_conv[0][0] \n", 437 | "__________________________________________________________________________________________________\n", 438 | "conv5_block1_1_relu (Activation (None, 7, 7, 512) 0 conv5_block1_1_bn[0][0] \n", 439 | "__________________________________________________________________________________________________\n", 440 | "conv5_block1_2_conv (Conv2D) (None, 7, 7, 512) 2359808 conv5_block1_1_relu[0][0] \n", 441 | "__________________________________________________________________________________________________\n", 442 | "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block1_2_conv[0][0] \n", 443 | "__________________________________________________________________________________________________\n", 444 | "conv5_block1_2_relu (Activation (None, 7, 7, 512) 0 conv5_block1_2_bn[0][0] \n", 445 | "__________________________________________________________________________________________________\n", 446 | "conv5_block1_0_conv (Conv2D) (None, 7, 7, 2048) 2099200 conv4_block6_out[0][0] \n", 447 | "__________________________________________________________________________________________________\n", 448 | "conv5_block1_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 conv5_block1_2_relu[0][0] \n", 449 | "__________________________________________________________________________________________________\n", 450 | "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048) 8192 conv5_block1_0_conv[0][0] \n", 451 | "__________________________________________________________________________________________________\n", 452 | "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048) 8192 conv5_block1_3_conv[0][0] \n", 453 | "__________________________________________________________________________________________________\n", 454 | "conv5_block1_add (Add) (None, 7, 7, 2048) 0 conv5_block1_0_bn[0][0] \n", 455 | " conv5_block1_3_bn[0][0] \n", 456 | "__________________________________________________________________________________________________\n", 457 | "conv5_block1_out (Activation) (None, 7, 7, 2048) 0 conv5_block1_add[0][0] \n", 458 | "__________________________________________________________________________________________________\n", 459 | "conv5_block2_1_conv (Conv2D) (None, 7, 7, 512) 1049088 conv5_block1_out[0][0] \n", 460 | "__________________________________________________________________________________________________\n", 461 | "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block2_1_conv[0][0] \n", 462 | "__________________________________________________________________________________________________\n", 463 | "conv5_block2_1_relu (Activation (None, 7, 7, 512) 0 conv5_block2_1_bn[0][0] \n", 464 | "__________________________________________________________________________________________________\n", 465 | "conv5_block2_2_conv (Conv2D) (None, 7, 7, 512) 2359808 conv5_block2_1_relu[0][0] \n", 466 | "__________________________________________________________________________________________________\n", 467 | "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block2_2_conv[0][0] \n", 468 | "__________________________________________________________________________________________________\n", 469 | "conv5_block2_2_relu (Activation (None, 7, 7, 512) 0 conv5_block2_2_bn[0][0] \n", 470 | "__________________________________________________________________________________________________\n", 471 | "conv5_block2_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 conv5_block2_2_relu[0][0] \n", 472 | "__________________________________________________________________________________________________\n", 473 | "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048) 8192 conv5_block2_3_conv[0][0] \n", 474 | "__________________________________________________________________________________________________\n", 475 | "conv5_block2_add (Add) (None, 7, 7, 2048) 0 conv5_block1_out[0][0] \n", 476 | " conv5_block2_3_bn[0][0] \n", 477 | "__________________________________________________________________________________________________\n", 478 | "conv5_block2_out (Activation) (None, 7, 7, 2048) 0 conv5_block2_add[0][0] \n", 479 | "__________________________________________________________________________________________________\n", 480 | "conv5_block3_1_conv (Conv2D) (None, 7, 7, 512) 1049088 conv5_block2_out[0][0] \n", 481 | "__________________________________________________________________________________________________\n", 482 | "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block3_1_conv[0][0] \n", 483 | "__________________________________________________________________________________________________\n", 484 | "conv5_block3_1_relu (Activation (None, 7, 7, 512) 0 conv5_block3_1_bn[0][0] \n", 485 | "__________________________________________________________________________________________________\n", 486 | "conv5_block3_2_conv (Conv2D) (None, 7, 7, 512) 2359808 conv5_block3_1_relu[0][0] \n", 487 | "__________________________________________________________________________________________________\n", 488 | "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block3_2_conv[0][0] \n", 489 | "__________________________________________________________________________________________________\n", 490 | "conv5_block3_2_relu (Activation (None, 7, 7, 512) 0 conv5_block3_2_bn[0][0] \n", 491 | "__________________________________________________________________________________________________\n", 492 | "conv5_block3_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 conv5_block3_2_relu[0][0] \n", 493 | "__________________________________________________________________________________________________\n", 494 | "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048) 8192 conv5_block3_3_conv[0][0] \n", 495 | "__________________________________________________________________________________________________\n", 496 | "conv5_block3_add (Add) (None, 7, 7, 2048) 0 conv5_block2_out[0][0] \n", 497 | " conv5_block3_3_bn[0][0] \n", 498 | "__________________________________________________________________________________________________\n", 499 | "conv5_block3_out (Activation) (None, 7, 7, 2048) 0 conv5_block3_add[0][0] \n", 500 | "__________________________________________________________________________________________________\n", 501 | "flatten_2 (Flatten) (None, 100352) 0 conv5_block3_out[0][0] \n", 502 | "__________________________________________________________________________________________________\n", 503 | "dense_2 (Dense) (None, 3) 301059 flatten_2[0][0] \n", 504 | "==================================================================================================\n", 505 | "Total params: 23,888,771\n", 506 | "Trainable params: 301,059\n", 507 | "Non-trainable params: 23,587,712\n", 508 | "__________________________________________________________________________________________________\n" 509 | ] 510 | } 511 | ], 512 | "source": [ 513 | "\n", 514 | "# view the structure of the model\n", 515 | "model.summary()\n" 516 | ] 517 | }, 518 | { 519 | "cell_type": "code", 520 | "execution_count": 75, 521 | "metadata": {}, 522 | "outputs": [], 523 | "source": [ 524 | "# tell the model what cost and optimization method to use\n", 525 | "model.compile(\n", 526 | " loss='categorical_crossentropy',\n", 527 | " optimizer='adam',\n", 528 | " metrics=['accuracy']\n", 529 | ")\n" 530 | ] 531 | }, 532 | { 533 | "cell_type": "code", 534 | "execution_count": 76, 535 | "metadata": {}, 536 | "outputs": [], 537 | "source": [ 538 | "# Use the Image Data Generator to import the images from the dataset\n", 539 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", 540 | "\n", 541 | "train_datagen = ImageDataGenerator(rescale = 1./255,\n", 542 | " shear_range = 0.2,\n", 543 | " zoom_range = 0.2,\n", 544 | " horizontal_flip = True)\n", 545 | "\n", 546 | "test_datagen = ImageDataGenerator(rescale = 1./255)" 547 | ] 548 | }, 549 | { 550 | "cell_type": "code", 551 | "execution_count": 77, 552 | "metadata": {}, 553 | "outputs": [ 554 | { 555 | "name": "stdout", 556 | "output_type": "stream", 557 | "text": [ 558 | "Found 64 images belonging to 3 classes.\n" 559 | ] 560 | } 561 | ], 562 | "source": [ 563 | "# Make sure you provide the same target size as initialied for the image size\n", 564 | "training_set = train_datagen.flow_from_directory('Datasets/Train',\n", 565 | " target_size = (224, 224),\n", 566 | " batch_size = 32,\n", 567 | " class_mode = 'categorical')" 568 | ] 569 | }, 570 | { 571 | "cell_type": "code", 572 | "execution_count": 78, 573 | "metadata": {}, 574 | "outputs": [ 575 | { 576 | "name": "stdout", 577 | "output_type": "stream", 578 | "text": [ 579 | "Found 58 images belonging to 3 classes.\n" 580 | ] 581 | } 582 | ], 583 | "source": [ 584 | "test_set = test_datagen.flow_from_directory('Datasets/Test',\n", 585 | " target_size = (224, 224),\n", 586 | " batch_size = 32,\n", 587 | " class_mode = 'categorical')" 588 | ] 589 | }, 590 | { 591 | "cell_type": "code", 592 | "execution_count": 79, 593 | "metadata": {}, 594 | "outputs": [ 595 | { 596 | "name": "stdout", 597 | "output_type": "stream", 598 | "text": [ 599 | "WARNING:tensorflow:sample_weight modes were coerced from\n", 600 | " ...\n", 601 | " to \n", 602 | " ['...']\n", 603 | "WARNING:tensorflow:sample_weight modes were coerced from\n", 604 | " ...\n", 605 | " to \n", 606 | " ['...']\n", 607 | "Train for 2 steps, validate for 2 steps\n", 608 | "Epoch 1/5\n", 609 | "2/2 [==============================] - 27s 14s/step - loss: 4.8157 - accuracy: 0.4062 - val_loss: 8.9823 - val_accuracy: 0.3276\n", 610 | "Epoch 2/5\n", 611 | "2/2 [==============================] - 22s 11s/step - loss: 6.7594 - accuracy: 0.5938 - val_loss: 12.2410 - val_accuracy: 0.3276\n", 612 | "Epoch 3/5\n", 613 | "2/2 [==============================] - 22s 11s/step - loss: 0.9376 - accuracy: 0.8906 - val_loss: 14.1595 - val_accuracy: 0.3276\n", 614 | "Epoch 4/5\n", 615 | "2/2 [==============================] - 21s 10s/step - loss: 1.1360 - accuracy: 0.9219 - val_loss: 15.6251 - val_accuracy: 0.3276\n", 616 | "Epoch 5/5\n", 617 | "2/2 [==============================] - 22s 11s/step - loss: 1.7595 - accuracy: 0.9062 - val_loss: 18.2626 - val_accuracy: 0.3276\n" 618 | ] 619 | } 620 | ], 621 | "source": [ 622 | "# fit the model\n", 623 | "# Run the cell. It will take some time to execute\n", 624 | "r = model.fit(\n", 625 | " training_set,\n", 626 | " validation_data=test_set,\n", 627 | " epochs=100,\n", 628 | " steps_per_epoch=len(training_set),\n", 629 | " validation_steps=len(test_set)\n", 630 | ")" 631 | ] 632 | }, 633 | { 634 | "cell_type": "code", 635 | "execution_count": 81, 636 | "metadata": {}, 637 | "outputs": [ 638 | { 639 | "data": { 640 | "image/png": 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\n", 641 | "text/plain": [ 642 | "

Find Your Car Brand by Simply uploading a Car Photo!

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Face Mask Detection system built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in video streams.