Traffic Sign Classifier
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German Traffic Sign Classification Project for Self-Driving Car Nano Degree Term 1. A CNN is designed and trained to detect the traffic signs using the German Traffic Sign Dataset. The system is also tested on German traffic signs to measure its performance. ...learn more
Project status: Published/In Market
Artificial Intelligence, Graphics and Media
Groups
Student Developers for AI,
DeepLearning,
Artificial Intelligence India
Overview / Usage
The Dataset used is German Traffic Signs Dataset which contains images of the shape (32x32x3) i.e. RGB images. I used the Numpy library to calculate summary statistics of the traffic signs data set:
The size of training set is 34799
The size of the validation set is 4410
The size of test set is 12630
The shape of a traffic sign image is (32, 32, 3)
The number of unique classes/labels in the data set is 43
Methodology / Approach
To train the model, I used: EPOCHS = 20, BATCH_SIZE = 128, rate = 0.001, mu = 0, sigma = 0.1. I used the same LeNet model architecture which consists of two convolutional layers and three fully connected layers. The input is an image of size (32x32x3) and output is 43 i.e. the total number of distinct classes. In the middle, I used RELU activation function after each convolutional layer as well as the first two fully connected layers. Flatten is used to convert the output of 2nd convolutional layer after pooling i.e. 5x5x16 into 400. Pooling is also done in between after the 1st and the 2nd convolutional layer. The training of the model is calculated in cell 13 and the model architecture is defined in cell 9.
Technologies Used
deep learning, tensorflow, machine learning
Repository
https://github.com/prateeksawhney97/Traffic-Sign-Classifier-Project-P3