self-driving-ish_computer_vision_system
This project generates images you've probably seen in autonomous driving demo. Object Detection, Lane Detection, Road Segmentation, Depth Estimation using TensorRT
Generate images for autonomous driving demos, including object and lane detection.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeThis project generates images you've probably seen in autonomous driving demo. Object Detection, Lane Detection, Road Segmentation, Depth Estimation using TensorRT
self-driving-ish_computer_vision_system has 407 stars on GitHub. It has been forked 88 times. self-driving-ish_computer_vision_system is written mainly in C++. It has been in active development since 2021. self-driving-ish_computer_vision_system is available under the Apache-2.0 license. Its main topics are autonomous-driving, cpp, deep-learning, tensorflow.
What is self-driving-ish_computer_vision_system?
This project generates images you've probably seen in autonomous driving demo. Object Detection, Lane Detection, Road Segmentation, Depth Estimation using TensorRT
Is self-driving-ish_computer_vision_system open source?
self-driving-ish_computer_vision_system is an open-source project. It is released under the Apache-2.0 license.
Is self-driving-ish_computer_vision_system free?
Yes. self-driving-ish_computer_vision_system is free and open source — you can use, modify and self-host it.
What license does self-driving-ish_computer_vision_system use?
self-driving-ish_computer_vision_system is available under the Apache-2.0 license.
What language is self-driving-ish_computer_vision_system written in?
self-driving-ish_computer_vision_system is written mainly in C++.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/iwatake2222-self-driving-ish-computer-vision-system.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.