GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → NVIDIA-AI-IOT → Jetracer

NVIDIA-AI-IOT / Jetracer

Licence: mit
An autonomous AI racecar using NVIDIA Jetson Nano

Labels

jupyter-notebook

Projects that are alternatives of or similar to Jetracer

Machine learning tutorials
Code, exercises and tutorials of my personal blog ! 📝
Stars: ✭ 601 (-2.44%)
Mutual labels:  jupyter-notebook
Info8010 Deep Learning
Lectures for INFO8010 - Deep Learning, ULiège
Stars: ✭ 608 (-1.3%)
Mutual labels:  jupyter-notebook
Amazon Sagemaker Examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
Stars: ✭ 6,346 (+930.19%)
Mutual labels:  jupyter-notebook
Fastai dev
fast.ai early development experiments
Stars: ✭ 604 (-1.95%)
Mutual labels:  jupyter-notebook
K Nearest Neighbors With Dynamic Time Warping
Python implementation of KNN and DTW classification algorithm
Stars: ✭ 604 (-1.95%)
Mutual labels:  jupyter-notebook
Instagram 3d Photo
A Chrome extension that adds a 3d photo effect to instagram pages.
Stars: ✭ 611 (-0.81%)
Mutual labels:  jupyter-notebook
Neuraltalk2
Efficient Image Captioning code in Torch, runs on GPU
Stars: ✭ 5,263 (+754.38%)
Mutual labels:  jupyter-notebook
Introduction to ml with python
Notebooks and code for the book "Introduction to Machine Learning with Python"
Stars: ✭ 5,843 (+848.54%)
Mutual labels:  jupyter-notebook
Stock Analysis Engine
Backtest 1000s of minute-by-minute trading algorithms for training AI with automated pricing data from: IEX, Tradier and FinViz. Datasets and trading performance automatically published to S3 for building AI training datasets for teaching DNNs how to trade. Runs on Kubernetes and docker-compose. >150 million trading history rows generated from +5000 algorithms. Heads up: Yahoo's Finance API was disabled on 2019-01-03 https://developer.yahoo.com/yql/
Stars: ✭ 605 (-1.79%)
Mutual labels:  jupyter-notebook
Early Stopping Pytorch
Early stopping for PyTorch
Stars: ✭ 612 (-0.65%)
Mutual labels:  jupyter-notebook
Cs231n spring 2017 assignment
My implementations of cs231n 2017
Stars: ✭ 603 (-2.11%)
Mutual labels:  jupyter-notebook
Challenges
PyBites Code Challenges
Stars: ✭ 604 (-1.95%)
Mutual labels:  jupyter-notebook
Pvnet
Code for "PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation" CVPR 2019 oral
Stars: ✭ 611 (-0.81%)
Mutual labels:  jupyter-notebook
Sqlitebiter
A CLI tool to convert CSV / Excel / HTML / JSON / Jupyter Notebook / LDJSON / LTSV / Markdown / SQLite / SSV / TSV / Google-Sheets to a SQLite database file.
Stars: ✭ 601 (-2.44%)
Mutual labels:  jupyter-notebook
Machinelearning
My blogs and code for machine learning. http://cnblogs.com/pinard
Stars: ✭ 5,984 (+871.43%)
Mutual labels:  jupyter-notebook
Deep learning cookbook
Deep Learning Cookbox
Stars: ✭ 601 (-2.44%)
Mutual labels:  jupyter-notebook
Ubuntu Ranking Dataset Creator
A script that creates train, valid and test datasets for the ranking task from Ubuntu corpus dialogs.
Stars: ✭ 609 (-1.14%)
Mutual labels:  jupyter-notebook
Breast cancer classifier
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Stars: ✭ 614 (-0.32%)
Mutual labels:  jupyter-notebook
H2o 3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Stars: ✭ 5,656 (+818.18%)
Mutual labels:  jupyter-notebook
Sigma coding youtube
This is a collection of all the code that can be found on my YouTube channel Sigma Coding.
Stars: ✭ 611 (-0.81%)
Mutual labels:  jupyter-notebook
View All Similar Projects ➔

JetRacer

JetRacer is an autonomous AI racecar using NVIDIA Jetson Nano. With JetRacer you will

  • Go fast - Optimize for high framerates to move at high speeds

  • Have fun - Follow examples and program interactively from your web browser

By building and experimenting with JetRacer you will create fast AI pipelines and push the boundaries of speed.

To get started, follow the setup below.

Cars

There are two different JetRacer cars that you can build. They differ primarily in size and speed. Which one to pick depends on your use case

Latrax Rally Tamiya TT02
1/18th scale 1/10th scale
Moderate Speed High Speed
~$400 total build cost ~$600 total build cost
Compact and portable Large and easy to modify
Soldering required No soldering required
Base car pre-assembled Base car assembly required

If you have any questions, please reach out by creating an issue.

Examples

JetRacer comes with a couple examples to get you up and running. The examples are in the format of Jupyter Notebooks, which are interactive documents which combine text, code, and visualization. Once you've completed the notebooks, start tweaking them to create your own racing software!

Example 1 - Basic motion

In this example you'll learn to progam JetRacer programatically from your web browser. Learn more in the examples documentation.

Example 2 - Road following

In this example, you'll teach JetRacer how to follow a road using AI. After training the neural network using the interactive training notebook, you'll optimize the model using NVIDIA TensorRT and deploy for a live demo. Learn more in the examples.

Setup

To get started with JetRacer, follow these steps

  1. Order parts from the bill of materials

  2. Follow the hardware setup

  3. Follow the software setup

  4. Run through the examples

See also

  • JetBot - An educational AI robot based on NVIDIA Jetson Nano

  • JetCam - An easy to use Python camera interface for NVIDIA Jetson

  • JetCard - An SD card image for web programming AI projects with NVIDIA Jetson Nano

  • torch2trt - An easy to use PyTorch to TensorRT converter

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].