I utilized deep neural networks and convolutional neural networks to classify traffic signs. I trained and validated a model so it can classify traffic sign images using the German Traffic Sign Dataset.

Solving German Traffic Signs Classification problem with VGG model

Self-Driving Nano Degree Program : Traffic Sign Recognition

German-Traffic-Sign-Classification

Traffic sign recognition project of Udacity Self-driving car nano degree

Classification of German traffic signs using deep neural network. Lenet is used as model to train on data set provided by Udacity. The result comprises prediction on downloaded traffic sign images.

A deep neural networks and convolutional neural network pipeline to classify different traffic signs from Kaggle German Traffic Sign Classification data. Applied Le-Net 5 Architecture.

Traffic Sign classifier based on TensorFLow

building a classifier to recognize traffic signs using deep neural networks and convolutional neural networks.

Third Project in the Udacity Self-Driving Car Nanodegree where Deep Neural Networks and Convolutional Neural Networks are used to classify traffic signs.

From traffic sign database downloaded from https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign It is produced using a CNN based on resnet and pytorch, a model to recognize traffic signs previously detected using the model produced at https://github.com/ablanco1950/DetectTrafficSign

Convolutional Neural Net to Classify Traffic Signs

Build a Traffic Sign Recognition Program