Basic jetson-inference repo stats
25 days ago

dusty-nv/jetson-inference is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.

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NOTE: The number of mentions on this list indicates mentions on common posts. Hence, a higher number means a better jetson-inference alternative or higher similarity.


Posts where jetson-inference has been mentioned. We have used some of these posts to build our list of alternatives and similar projects - the last one was on 2021-03-04.
  • Pretrained image classification model for nuts and bolts (or similar) | 2021-04-08
    Hello! I'm looking for some pre trained image classification models to use on a Jetson Nano. I already know about the model zoo and the pre trained models included in the repo. For demonstration purposes, however, I need a model trained on small objects from the context of production, ideally nuts, bolts, and similar small objects. Does anyone happen to know a source for this? Thanks a lot!
  • PyTorch 1.8 release with AMD ROCm support | 2021-03-04
    > They provide some SSD-Mobilenet-v2 here:

    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?

    > I get 140 fps on a Xavier NX

    That really is impressive. Holy shit.


    [1]: | 2021-03-04
    They provide some SSD-Mobilenet-v2 here:

    Also they want you to train it using their "DIGITS" interface which works but 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 coded in Pytorch and TF 2.x.

  • NVIDIA DLSS released as a plugin for Unreal Engine 4 | 2021-02-15
  • Help getting started
    If you have a screen and keyboard and mouse plugged into the Nano, I would recommend starting with Hello AI World on
  • I'm tired of this anti-Wayland horseshit | 2021-02-02
    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… that's 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). What proprietary drivers and libraries do I need to install? Nvidia basically says, here's a 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 resources, so using that OS image is no option.

    - 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.)

    - … (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 Github repo or some thread on the Nvidia forums[2]. (Though I have yet to find a reliably working wifi dongle…) But it usually takes you days orweeks to find it. From a product which is supposed to be industry-grade I would have expected more.


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