Dense layer (43 nodes, activation=”softmax”).Dense Fully connected layer (256 nodes, activation=”relu”). ![]() Flatten layer to squeeze the layers into 1 dimension.2 Conv2D layer (filter=64, kernel_size=(3,3), activation=”relu”).2 Conv2D layer (filter=32, kernel_size=(5,5), activation=”relu”).CNN is best for image classification purposes. To classify the images into their respective categories, we will build a CNN model ( Convolutional Neural Network). With the sklearn package, we use the train_test_split() method to split training and testing data.įrom the keras.utils package, we use to_categorical method to convert the labels present in y_train and t_test into one-hot encoding. The shape of data is (39209, 30, 30, 3) which means that there are 39,209 images of size 30×30 pixels and the last 3 means the data contains colored images (RGB value). We need to convert the list into numpy arrays for feeding to the model. The PIL library is used to open image content into an array.įinally, we have stored all the images and their labels into lists (data and labels). With the help of the OS module, we iterate over all the classes and append images and their respective labels in the data and labels list. Our ‘train’ folder contains 43 folders each representing a different class. Our approach to building this traffic sign classification model is discussed in four steps: To get started with the project, download and unzip the file from this link – Traffic Signs Recognition Zip FileĪnd extract the files into a folder such that you will have a train, test and a meta folder.Ĭreate a Python script file and name it traffic_signs.py in the project folder. To install the necessary packages used for this Python data science project, enter the below command in your terminal: pip install tensorflow keras sklearn matplotlib pandas pilĬheck out 270+ Free Python Tutorials Steps to Build the Python Project This project requires prior knowledge of Keras, Matplotlib, Scikit-learn, Pandas, PIL and image classification. The dataset has a train folder which contains images inside each class and a test folder which we will use for testing our model. The size of the dataset is around 300 MB. The dataset is quite varying, some of the classes have many images while some classes have few images. It is further classified into 43 different classes. The dataset contains more than 50,000 images of different traffic signs. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles.įor this project, we are using the public dataset available at Kaggle: In this Python project example, we will build a deep neural network model that can classify traffic signs present in the image into different categories. Traffic Signs Recognition – About the Python Project Traffic signs classification is the process of identifying which class a traffic sign belongs to. ![]() There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic Signs Recognition Python Project.Driver Drowsiness Detection Python Project. ![]()
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