jetson-ffmpeg VS jetson-inference

I made my own build of frigate using the community nvmpi ffmpeg decoder. What I didn't realise when I bought Jetson is normal Nvidia nvdec ffmpeg is not supported on Jetsons, and nvidia did little to enable their different hardware decoder for Jetsons, and concentrated their efforts on gstreamer instead of ffmpeg. So you need to use 3rd party nvmpi extension for ffmpeg https://github.com/jocover/jetson-ffmpeg . Also I debloat Frigate ffmpeg build. I spent a lot of time trying to get the hardware decoder working on rk3399 before giving up and getting a jetson nano. Reducing build time by removing all the unnecessary codecs is helpful for testing. https://pastebin.com/bxWwDz0K is my ffmpeg config for Frigate. Copy and create a new build in Frigate Makefile for aarch64nvmpi based off the aarch64 config. Make that config use the ffmpeg config specific to nvmpi.

I purchased the jetson nano because the initial specs indicated that real time encoding for 1080p 60fps was possible. Later did I figure out that the jetson nano has a different driver for their onboard gpu different than the desktop gpus. The desktop gpu use nvenc dedicated hardware for encoding that works with ffmpeg, alas for the jetson nano, nvidia does not support ffmpeg hardware accelerated encoding out of the box, only decoding. But, someone actually came up with a solution to include the nvmpi lib that utilizes the nvenc hardware acceleration for encoding. https://github.com/jocover/jetson-ffmpeg

Jetson Nano’s are obsolete and no longer supported; but to answer your question, this might be a good place to start.

I’m trying to do the tutorial, where they retrain the neural network to detect fruits (jetson-inference/pytorch-ssd.md at master · dusty-nv/jetson-inference · GitHub 1)

Jetson-Inference is another amazing resource to get started on. This will allow you to try out a number of neural networks (classification, detection, and segmentation) all with your own data or with sample images included in the repo.

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 https://github.com/dusty-nv/jetson-inference 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!

> 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?

> 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...

If you have a screen and keyboard and mouse plugged into the Nano, I would recommend starting with Hello AI World on https://github.com/dusty-nv/jetson-inference#hello-ai-world

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.

[0]: https://github.com/dusty-nv/jetson-inference

https://github.com/dusty-nv/jetson-inference#system-setup