alex-lechner/Traffic-Light-Classification

A detailed tutorial on how to build a traffic light classifier with TensorFlow for the capstone project of Udacity's Self-Driving Car Engineer Nanodegree Program.

48
/ 100
Emerging

This project offers a detailed guide for engineers developing self-driving car systems to accurately identify the state of traffic lights. It uses various image datasets of traffic lights and applies machine learning techniques to classify them as red, yellow, or green. The output is a highly precise traffic light classifier, crucial for autonomous navigation systems.

125 stars. No commits in the last 6 months.

Use this if you are a self-driving car engineer who needs to train a robust traffic light classification model for an autonomous vehicle's perception system.

Not ideal if you're looking for a plug-and-play solution without understanding the underlying training process or if your domain is not related to self-driving cars.

self-driving cars autonomous vehicles traffic light detection perception systems robotics
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 22 / 25

How are scores calculated?

Stars

125

Forks

55

Language

Jupyter Notebook

License

MIT

Last pushed

Apr 27, 2018

Commits (30d)

0

Get this data via API

curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/alex-lechner/Traffic-Light-Classification"

Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.