The project revolves around creating a deep neural network that can classify traffic signs present in the image into different categories.
With this model, we are able to read and understand traffic signs with 98% accuracy.
Our approach to building this traffic sign classification model is discussed in four steps:
- Exploring the dataset and some visualizations
- Splitting the dataset into training and testing data
- Building the CNN model
- Training and validating the model
- Testing the model with the testing dataset
