Classify traffic signs by three classic ConvNets architecture using GTSRB dataset.

A traffic sign classifier built with TensorFlow

A traffic sign classifier using LeNet for Self driving cars

This Python script generates a synthetic dataset of traffic sign images in COCO format, intended for training and testing object detection models. The dataset includes various traffic sign overlays placed on diverse background images, offering a wide range of scenarios to enhance model robustness.

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.

This program uses a deep neural network with several convolutional layers to classify traffic signs. The model is able to recognize traffic signs with an accuracy of 96,2%. It was trained and validated using the German Traffic Sign Dataset with 43 classes (types of traffic signs) and more than 50,000 images in total.

Traffic Sign Classifier using a convolutional neural network

My solution to the Udacity Self-Driving Car Engineer Nanodegree Traffic Sign Classifier project.

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

Traffic Sign Classification with Convolutional Neural Networks in Python

Udacity: Self-Driving Car Engineer Nanodegree | Project: Traffic Sign Classification

Traffic-Sign-Classifier

Traffic sign classifier using OpenCV, LeNet-5, AlexNet

Created a traffic sign classifier with a CNN.

카메라 이미지의 신호등 인식 기술 개발

Self Driving Car NanoDegree - Project 2 - Traffic Sign Classifier

Classify the Trafic Signs using deep learning model with Tensorflow

Traffic Sign Classifier Project for Self-Driving Car ND

Traffic sign classification with a convolutional neural network build with tensorflow