Traffic Vision

A Deep Learning Project for Traffic Sign Recognition using CNN, Tensorflow and Keras - Achieves 94.8% accuracy

Architecture

• Conv2D layer (filter=32, kernel_size=(5,5), activation=”relu”)
• MaxPool2D layer ( pool_size=(2,2))
• Dropout layer (rate=0.25)
• 2 Conv2D layer (filter=64, kernel_size=(3,3), activation=”relu”)
• MaxPool2D layer ( pool_size=(2,2))
• Dropout layer (rate=0.25)
• Flatten layer to squeeze the layers into 1 dimension
• Dense Fully connected layer (256 nodes, activation=”relu”)
• Dropout layer (rate=0.5)
• Dense layer (43 nodes, activation=”softmax”)

Metrics