https://github.com/thibo73800/capsnet-traffic-sign-classifier

A Tensorflow implementation of CapsNet(Capsules Net) apply on german traffic sign dataset
https://github.com/thibo73800/capsnet-traffic-sign-classifier

capsnet capsule-network capsules-net convolutional-neural-networks deep-learning neural-network tensorflow

Last synced: about 1 month ago
JSON representation

A Tensorflow implementation of CapsNet(Capsules Net) apply on german traffic sign dataset

• Host: GitHub
• URL: https://github.com/thibo73800/capsnet-traffic-sign-classifier
• Owner: thibo73800
• License: apache-2.0
• Created: 2017-11-06T18:13:35.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-03-05T14:58:34.000Z (over 6 years ago)
• Last Synced: 2024-02-18T05:31:38.872Z (5 months ago)
• Topics: capsnet, capsule-network, capsules-net, convolutional-neural-networks, deep-learning, neural-network, tensorflow
• Language: Jupyter Notebook
• Size: 1.31 MB
• Stars: 177
• Watchers: 17
• Forks: 90
• Open Issues: 12
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-CapsNet - thibo73800/capsnet-traffic-sign-classifier
• awesome-capsule-networks - thibo73800/capsnet-traffic-sign-classifier

README

        
# Capsnet - Traffic sign classifier - Tensorflow

A Tensorflow implementation of CapsNet(Capsules Net) apply on the German traffic sign dataset

[![Contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=plastic)](CONTRIBUTING.md)

[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg?style=plastic)](https://opensource.org/licenses/Apache-2.0)

![completion](https://img.shields.io/badge/completion%20state-80%25-blue.svg?style=plastic)

This implementation is based on this paper: Dynamic Routing Between Capsules (https://arxiv.org/abs/1710.09829) from Sara Sabour, Nicholas Frosst and Geoffrey E. Hinton.

This repository is a work in progress implementation of a Capsules Net. Since I am using a different dataset (Not MNIST) some details in the architecture are different. The code for the CapsNet is located in the following file: caps_net.py while the whole model is created inside the model.py file. The two main methods used to build the CapsNet are  conv_caps_layer and fully_connected_caps_layer



## Requirements

- Python 3

- NumPy 1.13.1

- Tensorflow 1.3.0

- docopt 0.6.2

- Sklearn: 0.18.1

- Matplotlib

## Install

    $> git clone https://github.com/thibo73800/capsnet_traffic_sign_classifier.git

    $> cd capsnet_traffic_sign_classifier.git

    $> wget https://d17h27t6h515a5.cloudfront.net/topher/2017/February/5898cd6f_traffic-signs-data/traffic-signs-data.zip

    $> unzip traffic-signs-data.zip

    $> mkdir dataset

    $> mv *.p dataset/

    $> rm traffic-signs-data.zip

   

## Train

    $> python train.py -h

    $> python train.py dataset/

During the training, the checkpoint is saved by default into the outputs/checkpoints/ folder. The exact path and name of the checkpoint is print during the training.

## Test

In order to measure the accuracy and the loss on the Test dataset you need to used the test.py script as follow:

    $> python test.py outputs/checkpoints/ckpt_name dataset/ 

## Metrics / Tensorboard

Accuracy: 



    
Train: 99%

    
Validation: 98%

    
Test: 97%



Checkpoints and tensorboard files are stored inside the outputs folder.



Exemple of some prediction: