Building a CNN based traffic signs classifier.

论文 Hinton等的论文 Matrix capsules with EM routing - Hinton, G. E., Sabour, S. and Frosst, N. (2018) Dynamic Routing Between Capsules - Sabour, S., Frosst, N. and Hinton, G.E. (2017) Transforming Auto-encoders - Hinton, G. E., Krizhevsky, A. and Wang, S. D. (2011) A parallel computation that assigns canonical object-based frames of reference. - Hinton, G.E. (1981) Shape representation in parallel systems - Hinton, G.E. (1981) Optimizing Neural Networks that Generate Images - Tijmen Tieleman’s disseration 其他论文 Capsule Network Performance on Complex Data - Xi, E., Bing, S. and Jin, Y. (2017) Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture - Qiao, K., Zhang, C., Wang, L., Yan, B., Chen, J., Zeng, L. and Tong, L., (2018) An Optimization View on Dynamic Routing Between Capsules - Wang, D., Liu, E. (2018) CapsuleGAN: Generative Adversarial Capsule Network Ayush Jaiswal, Wael AbdAlmageed, Premkumar Natarajan. (2018) Spectral Capsule Networks - Bahadori, M. T. (2018) 博客 Max Pechyonkin的胶囊网络入门系列： 胶囊网络背后的直觉 胶囊如何工作 囊间动态路由算法 胶囊网络架构 基于TensorFlow实现胶囊网络 Debarko De的胶囊网络教程，包括注释详尽的胶囊网络实现代码 基于CUDA为胶囊网络实现TensorFlow定制操作 Jos van de Wolfshaar的文章，定制胶囊网络运算的CUDA支持 ISI新研究：胶囊生成对抗网络 用胶囊网络替换CNN作为GAN的判别网络，在MNIST数据集上取得了比卷积GAN更好的表现 Uncovering the Intuition behind Capsule Networks and Inverse Graphic Tanay Kothari的长篇教程 A Visual Representation of Capsule Connections in Dynamic Routing Between Capsules Mike Ross的胶囊网络示意图 Capsule Networks Are Shaking up AI — Here’s How to Use Them Nick Bourdakos的介绍 Capsule Networks Explained Kendrick Tan的解释 Understanding Dynamic Routing between Capsules (Capsule Networks) Jonathan Hui的教程，包括注释详尽的基于Keras的胶囊网络实现代码 Matrix capsules with EM routing Adrian Colyer关于EM路由的文章 Capsule Networks: A Glossary Sebastian Kwiatkowski的胶囊网络术语表 Overview of awesome articles 点评三篇胶囊网络教程 视频 Geoffrey Hinton’s talk: What is wrong with convolutional neural nets? - Geoffrey Hinton在MIT. Brain & Cognitive Sciences的演讲《卷积神经网络的问题在哪里？》 Capsule Networks (CapsNets) – Tutorial - “这视频棒极了。我本希望我能把胶囊解释得这么清楚。”Geoffrey Hinton Capsule networks: overview - 胶囊网络概览，包括向量和矩阵胶囊。 Overview of awesome videos 对以上3个视频的点评。 Capsule Networks: An Improvement to Convolutional Networks Siraj Raval介绍胶囊网络的视频 动态路由实现 官方实现 Sarasra/models 《Dynamic Routing Between Capsules》论文所用的代码 TensorFlow alisure-ml/CapsNet bourdakos1/capsule-networks etendue/CapsNet_TF InnerPeace-Wu/CapsNet-tensorflow jaesik817/adv_attack_capsnet jostosh/capsnet JunYeopLee/capsule-networks laodar/tf_CapsNet leoniloris/CapsNet naturomics/CapsNet-Tensorflow rrqq/CapsNet-tensorflow-jupyter thibo73800/capsnet-traffic-sign-classifier tjiang31/CapsNet winwinJJiang/capsNet-Tensorflow PyTorch acburigo/CapsNet adambielski/CapsNet-pytorch AlexHex7/CapsNet_pytorch aliasvishnu/Capsule-Networks-Notebook-MNIST andreaazzini/capsnet.pytorch cedrickchee/capsule-net-pytorch dragen1860/CapsNet-Pytorch gram-ai/capsule-networks higgsfield/Capsule-Network-Tutorial laubonghaudoi/CapsNet_guide_PyTorch leftthomas/CapsNet nishnik/CapsNet-PyTorch tonysy/CapsuleNet-PyTorch Ujjwal-9/CapsNet Keras fengwang/minimal-capsule gusgad/capsule-GAN mitiku1/Emopy-CapsNet ruslangrimov/capsnet-with-capsulewise-convolution streamride/CapsNet-keras-imdb sunxirui310/CapsNet-Keras theblackcat102/dynamic-routing-capsule-cifar XifengGuo/CapsNet-Keras XifengGuo/CapsNet-Fashion-MNIST Chainer soskek/dynamic_routing_between_capsules Torch mrkulk/Unsupervised-Capsule-Network MXNet AaronLeong/CapsNet_Mxnet GarrickLin/Capsnet.Gluon Soonhwan-Kwon/capsnet.mxnet CNTK Southworkscom/CapsNet-CNTK Lasagne DeniskaMazur/CapsNet-Lasagne Matlab yechengxi/LightCapsNet R dfalbel/capsnet JavaScript alseambusher/capsnet.js Vulcan moothyknight/CapsNet-for-Graphics-Rendering-Optimization EM路由实现 TensorFlow gyang274/capsulesEM www0wwwjs1/Matrix-Capsules-EM-Tensorflow PyTorch shzygmyx/Matrix-Capsules-pytorch 其他资源 Capsule Networks discussion Facebook讨论组 CapsNet-Tensorflow CapsNet-Tensorflow的gitter.im讨论组 Will capsule networks replace neural networks? Quora问答“胶囊网络会取代神经网络吗？” Could GANs work with Hinton’s capsule theory? Quora问答“GAN可以应用Hinton的胶囊理论吗？” Dynamic Routing Between Capsules Kyuhwan Jung对论文《Dynamic routing between Capsules》的评论（slideshare）

End-to-end deep learning project for classifying German traffic signs using CNNs, trained on the GTSRB dataset with preprocessing, evaluation, and inference pipeline.

CNN to detect and classify traffic signs

Notebook and model for German Traffic Sign Recognition Benchmark (GTSRB) Dataset. The notebook contains an extensive EDA for the dataset and trains a CNN classifier on the benchmark.

Traffic sign classifier project by cnn

An application built with TensorFlow and Keras for traffic sign detection. Utilizes Convolutional Neural Networks (CNNs) to accurately identify and classify traffic signs from images. Achieved an accuracy of 98.89% on the test dataset. Simply upload images to classify traffic signs. Contributions welcome!

Using a deep convolutional neural network (CNN) as a feature encoder (or backbone) is the most commonly observed architectural pattern in several computer vision methods, and semantic segmentation is no exception. The two major drawbacks of this architectural pattern are: (i) the networks often fail to capture small classes such as wall, fence, pole, traffic light, traffic sign, and bicycle, which are crucial for autonomous vehicles to make accurate decisions. (ii) due to the arbitrarily increasing depth, the networks require massive labeled data and additional regularization techniques to converge and to prevent the risk of over-fitting, respectively. While regularization techniques come at minimal cost, the collection of labeled data is an expensive and laborious process. In this work, we address these two drawbacks by proposing a novel lightweight architecture named point-wise dense flow network (PDFNet). In PDFNet, we employ dense, residual, and multiple shortcut connections to allow a smooth gradient flow to all parts of the network. The extensive experiments on Cityscapes and CamVid benchmarks demonstrate that our method significantly outperforms baselines in capturing small classes and in few-data regimes. Moreover, our method achieves considerable performance in classifying out-of-the training distribution samples, evaluated on Cityscapes to KITTI dataset.

Use CNN to classify traffic sign

Classifying Different Traffic Signs Using CNN

Traffic sign classifier based on Multi-Scale CNNs

Traffic Sign Classifier using Deep Neural Networks and CNN.

A Convolutional Neural Network (CNN) for German traffic sign classification using the GTSRB dataset. This project classifies German traffic signs into 43 different categories, providing a high-accuracy solution for recognizing and distinguishing road signs.

The Traffic Sign Detection and Recognition Using CNN project enhances road safety by using Convolutional Neural Networks to automatically detect and classify traffic signs. It improves accuracy and speed, making it ideal for autonomous driving and real-time traffic management systems.

This project preprocesses a dataset for traffic sign classification, implementing a Convolutional Neural Network (CNN) to classify the signs. It evaluates the model's performance using metrics such as accuracy, F1 score, recall, and precision.

Train deep learning models known as Convolutional Neural Networks (CNNs) to classify 43 traffic sign images. This project could be practically applied to self-driving cars.

Training CNN model for classifying traffic sign

This is a project trying to use CNNs to build a classifier for German Traffic Signs Dataset!

CNN and Data Augmentation to train a traffic sign classifier with OpenCv and Tensorflow

A deep learning project for classifying traffic sign categories using Convolutional Neural Networks (CNNs).

This repository contains a Convolutional Neural Network (CNN) model developed to classify images of German traffic signs using the German Traffic Sign dataset. This project demonstrates advanced techniques in deep learning, including image preprocessing, CNN model architecture, and performance evaluation.

The traffic sign recognition system (TSRS) is an important component of an intelligent transportation system (ITS). Being able to interpret traffic signs properly and efficiently can increase driving safety. This project proposes a traffic sign identification approach based on deep learning, which primarily targets at the detection and classification of traffic signs while being trained on a traffic sign benchmark dataset. A traffic sign recognition and identification approach based on image processing is proposed, which is integrated with a convolutional neural network (CNN) to classify traffic signs. TensorFlow is used to implement CNN. We have 99.4% accuracy in identifying. Keywords - Traffic Sign Recognition System (TSRS), Detection & Classification, Image Processing, CNN, TensorFlow

In this case study, we want to classify images of traffic signs using deep Convolutional Neural Networks (CNNs). The dataset consists of 43 different classes of images.

The main objective of our project is to design and construct a computer based system which can automatically detect the road signs so as to provide assistance to the user or the machine so that they can take appropriate actions.The proposed approach consists of building a model using convolutional neural networks by extracting traffic signs from an image using color information.We have used convolutional neural networks (CNN) to classify the traffic signs and we used color based segmentation to extract/crop signs from images

This project is to recognize the traffic signs in the wild (real-world) which is one of the main tasks for any self-driving car project. It is a computer vision classification problem that I’ve tackled by building a CNN model trained from scratch to do the job.

Traffic sign classifier using CNN

Classify Traffic Signs using LeNet CNN

traffic signs classifier using CNN and Lenet architecture

Traffic Sign classifier using CNN and TensorFlow

Use CNN to classify traffic sign