vikramnagashoka / traffic-sign-classification

Traffic-Sign-Classification

Objective

Design, train and test a neural network model for German Traffic Signs classification

Data set Summary

Size of Training Set: 34799 samples Size of Validation Set: 4410 samples Size of Test Set: 12630 samples Shape of a sample: (32,32,3) Number of unique classes: 43

Pre-processing the data

Step 1: Added additional training samples by adding noise to the existing images.

Step 2: Converted all the training samples to grayscale because the result is not dependent on the color channels.

Step 3: Normalized the data with the formula (image_data-128)/128. This is done so as to avoid impact of images with big weights.

Network architecture

The Layers in the network are as below:

Layer 1:

Type: Convolutional. 
Input = 32x32x1. Output = 28x28x6.
Pooling: Input = 28x28x6. Output = 14x14x6.

Layer 2:

Type: Convolutional
Input =14x14x6 Output = 10x10x16
Pooling : Input = 10x10x16. Output = 5x5x16

Layer 3:

Type: Convolutional
Input = 5x5x16. Output = 1x1x32

Layer 4:

Type:Fully connected
Input = 32. Output = 120

Layer 5:

Type:Fully connected
Input = 120. Output = 84

Layer 6:

Type:Fully connected
Input = 84. Output = 32

Layer 7:

Type:Fully connected
Input = 32. Output = 43