In this project, I used Python and TensorFlow to classify traffic signs. Dataset used: German Traffic Sign Dataset. This dataset has more than 50,000 images of 43 classes. I was able to reach a +99% validation accuracy, and a 97.3% testing accuracy.

German Traffic Sign Recognition Benchmark (GTSRB) AlexNet pycaffe model. http://benchmark.ini.rub.de/

GTSRB - German Traffic Sign Recognition

German Traffic Sign Classification Project for Self-Driving Car Nano Degree Term 1. A CNN is designed and trained to detect the traffic signs using the German Traffic Sign Dataset. The system is also tested on German traffic signs to measure its performance.

Detect The German Traffic Sign Using YoloV5

Recognizing Traffic signs from GTSRB dataset ||:red_circle:||:small_red_triangle:||:small_red_triangle_down:||

This is a Classifier Algorithm that can classify German Traffic-Signs. It uses the good old convolution network inspired by the Nvidia Model used in their self-driving car.

Traffic Sign Classifier using Deep Neural Networks and CNN.

Build a Convolutional Neural Network (CNN) that recognizes traffic signs.

Udacity Self Driving Car Projects

Ford Otosan Internship Project 2021 || Traffic Sign Detection and Recognition.

German-Traffic-Sign-Classification

Traffic Sign Classifier using the German Traffic Sign Dataset

German Traffic Sign Classification using Constitutional Neural Networks, uses Image Augmentation and pre-processing of images

German Traffic Signs Classification

Multi-class, single-image classification using MobileNet-CNN

Predicting the class of traffic signs from the german traffic sign dataset with TensorFlow

Prediction Analysis on TSR using CNN with 99% accuracy