dusty-nv

jetson-inference

C++2017–2021

dusty-nv / jetson-inference

jetson-inference

Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

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9KGitHub stars

REPOSITORY AT A GLANCE

What to know before exploring

What it is

Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

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This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

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Mentions over time

HN mentions by year4 dated mentions

Observed mentions: 2017-11-08 01:28:52 UTC to 2021-03-07 17:41:37 UTC.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

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Period	Start (UTC)	End (exclusive, UTC)	Mentions	Comments
2017	2017-01-01 00:00:00 UTC	2018-01-01 00:00:00 UTC	1	1
2018	2018-01-01 00:00:00 UTC	2019-01-01 00:00:00 UTC	0	0
2019	2019-01-01 00:00:00 UTC	2020-01-01 00:00:00 UTC	0	0
2020	2020-01-01 00:00:00 UTC	2021-01-01 00:00:00 UTC	0	0
2021	2021-01-01 00:00:00 UTC	2022-01-01 00:00:00 UTC	3	3

What Hacker News says

4 sources

codethief·Mar 7, 2021

> They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference I was aware of that repository but from taking a cursory look at it I had thought dusty was just converting models from PyTorch to TensorRT, like here[0, 1]. Am I missing something? (EDIT: Oh, never mind. You probably meant the model trained on COCO[2]. Now I remember that I ignored it way back when because I needed much better accuracy.) > I get 140 fps on a Xavier NX That really is impressive. Holy shit. [0]: https://github.com/dusty-nv/jetson-inference/blob/master/doc... [1]: https://github.com/dusty-nv/jetson-inference/issues/896#issu... [2]: https://github.com/dusty-nv/jetson-inference/blob/master/doc...

In PyTorch 1.8, with AMD ROCm support

dheera·Mar 6, 2021

They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference Yeah, it works. I get 140 fps on a Xavier NX. It's super impressive for the wattage and size of the device. But they want you to train it using their horrid "DIGITS" interface, and it doesn't support any more recent networks. I really wish Nvidia would stop trying to reinvent the wheel in training and focus on keeping up with being able to properly parse all the operations in the latest state-of-the-art networks which are almost always in Pytorch or TF 2.x.

In PyTorch 1.8, with AMD ROCm support

codethief·Feb 13, 2021

Well, don't get me wrong. I do like my Jetson Nano. For a hobbyist who likes to tinker with machine learning in their spare time it's definitely a product cool and there are quite a few repositories on Github[0, 1] with sample code. Unfortunately… I'm not a hobbyist, I'm not tinkering and the aforementioned repositories are… about it. There is little documentation about - how to build a custom OS image (necessary if you're thinking about using Jetson as part of your own product, i.e. a large-scale deployment). Ideally, I would like to do that as part of a CI/CD workflow. What proprietary drivers and libraries do I need to install? Nvidia basically says, here's our custom Ubuntu image with the usual GUI, complete driver stack and everything – take it or leave it. Unfortunately, the GUI alone is eating up a lot of the precious CPU and GPU r

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esources, so using their OS image in production is no option for me. - how deployment works on production modules (as opposed to the non-production module in the Developer Kit) - what production modules are available in the first place ("Please refer to our partners") - what wifi dongles are compatible (the most recent Jetson Nano comes w/o wifi) - how to convert your custom models to TensorRT, what you need to pay attention to etc. (The official docs basically say: Have a look at the following nondescript sample code. Good luck.) - how do I automize testing of TensorRT models & code? Why isn't there a compatibility layer which allows executing my models on non-Jetson devices / on a CPU (obviously at lower performance, but I don't mind). - … (I'm sure I'm forgetting many other things that I've struggled with over the past months) Anyway. It's not that this information isn't out there somewhere in some blog post, some obscure Github repo or some thread on the Nvidia forums[2]. (Though I have yet to find a wifi dongle that works reliably…) But it usually takes you days or weeks to find it. From a product which is supposed to be industry-grade I would have expected more. [0]: https://github.com/dusty-nv/jetson-inference [1]: https://github.com/jkjung-avt/tensorrt_demos [2]: https://forums.developer.nvidia.com/c/agx-autonomous-machine...

In I'm tired of this anti-Wayland horseshit

joshvm·Nov 8, 2017

The Jetson TX2 can run GoogLeNet in real-time with the onboard camera, so it's definitely possible on mainstream GPUs too. https://github.com/dusty-nv/jetson-inference Inspecting layer activations in real-time is trickier, but presumably possible.

In Feature Visualization: How neural nets build up their understanding of images