Traffic Sign Recognition with TensorFlow and SageMaker

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Intro

This project trains a TensorFlow model in SageMaker for traffic sign recognition.

The files contained in this repo includes:

• readme.md
• Traffic_sign_recognition_with_TensorFlow.ipynb
• train.py

Run Traffic_sign_recognition_with_TensorFlow.ipynb in SageMaker as a notebook instance.

Dataset

Necessary files not included in this repo can be downloaded from Kaggle: GTSRB - German Traffic Sign Recognition Benchmark.

Here is an overview of the dataset:

• Each image has slightly different sizes
• Number of training examples = 39209
• Number of testing examples = 12630
• Number of classes = 43

Model

The network architecture is similar to LeNet and the details of each layer is as follows:

Layer	Description
Input	32x32x1 Grayscale image
Convolution 5x5	1x1 stride, valid padding, outputs 28x28x6
RELU
Max pooling	2x2 stride, outputs 14x14x6
Convolution 5x5	1x1 stride, valid padding, outputs 10x10x16
RELU
Max pooling	2x2 stride, outputs 5x5x6
Fully connected	Input = 400. Output = 120.
Fully connected	Input = 120. Output = 84.
Dropout
Fully connected	Input = 84. Output = 43.
Softmax	Output layer

See complete tutorial here