raymas / German-Traffic-Sign-Recognition-Benchmark

Open source neural network solutions for the GTSRB challenge

German Traffic Sign Recognition Benchmark (GTSRB)

This repository aims to implement classifiers networks models from the GTSRB challenge.

Getting started

Clone this repository:

git clone https://github.com/raymas/German-Traffic-Sign-Recognition-Benchmark.git

Prior training networks, there are prerequisites.

Docker installation (Work in progress)

Simply build using the provided Dockerfile:

docker build -t gtsrb-nn .
docker run -it gtsrb-nn [args]

See How to use for correct list of arguments. Please note this docker image is using the GPU version of tensorflow.

Conda environnement

Create a new virtual environnement using the lastest tensorflow packages (GPU or not) from anaconda.

conda create -c anaconda tensorflow[-gpu] pip

PS: please remove the '[]' if you want to have to gpu acceleration or simply delete '[-gpu]' for cpu only tensorflow.

Install the required softwares:

pip install -r requirements.txt

How to use

Train and test by launching one of the provided model:

python main.py --model DKS --train

For help:

python main.py -h

With docker :

docker run -it gtsrb-nn --model DKS --train

Results

DeepKnowledgeSeville

After 30 epochs :

• Accuracy : ~ 95%
• Loss : 0.07

Loss	Accuracy

TODO

• Spatial extractor.

Contributing

Fork, publish a new branch, add your name to CONTRIBUTORS.md

Sources

GTSRB J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. The German Traffic Sign Recognition Benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 1453–1460. 2011.

DeepKnowledge Seville CNN with 3 Spatial Transformers, DeepKnowledge Seville, Álvaro Arcos-García and Juan A. Álvarez-García and Luis M. Soria-Morillo, Neural Networks

Sermanet Multi-Scale CNNs, sermanet , Traffic sign recognition with multi-scale Convolutional Networks, Traffic sign recognition with multi-scale Convolutional Networks, P. Sermanet, Y. LeCun, August 2011, International Joint Conference on Neural Networks (IJCNN) 2011