GTSRB-torch

Image classification using pytorch and fastai on German Traffic Sign data set

Project: Build a Traffic Sign Recognition Program

Overview

In this project, you will use what you've learned about deep neural networks and convolutional neural networks to classify traffic signs. You will train and validate a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model is trained, you will then try out your model on images of German traffic signs that you find on the web.

Dependencies

This project requires

[pytorch]

Dataset and Repository

Download the data set.
Clone the project

git clone https://github.com/gautam-sharma1/GTSRB-torch.git
cd GTSRB-torch
python main.py

Model Architecture

My final model consisted of the following layers:

Layer	Description
Input	32x32x3 RGB image
Convolution 5x5	1x1 stride, same padding, outputs 28x28x100
RELU
Convolution 3x3	1x1 stride, same padding, outputs 14x14x150
RELU
Convolution 1x1	1x1 stride, same padding, outputs 8x8x250
RELU
Max Pool 2x2	2x2 stride, outputs 3x3x250
Fully connected	Input = 250X3X3, Output = 350
RELU
Dropout	keep_prob = 0.5
Fully connected	Input = 350, Output = 43
Softmax	43 classes

To train the model, I used a learning rate of 0.001 with 10 EPOCHS and a Batch Size of 128. I also used SGDAdam Optimizer as it tends to perform better than SGD.

Test a Model on New Images

1. Choose five German traffic signs found on the web and provide them in the report. For each image, discuss what quality or qualities might be difficult to classify.

Here are five German traffic signs that I found on the web:

ggsharma/GTSRB-torch