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> I'll also add a caveat that toolage for Jetson boards is extremely incomplete. A hundred times this. I was about to write another rant here but I already did

> I'll also add a caveat that toolage for Jetson boards is extremely incomplete. A hundred times this. I was about to write another rant here but I already did that[0] a while ago, so I'll save my breath this time. :) Another fun fact regarding toolage: Today I discovered that many USB cameras work poorly on Jetsons (at least when using OpenCV), probably due to different drivers and/or the fact that OpenCV doesn't support ARM64 as well as it does x86_64. :( > They supply you with a bunch of sorely outdated models for TensorRT like Inceptionv3 and SSD-MobileNetv2 and VGG-16. They supply you with such models? That's news to me. AFAIK converting something like SSD-MobileNetv2 from TensorFlow to TensorRT still requires substantial manual work and magic, as this code[1] attests to. There are countless (countless!) posts on the Nvidia forums by people complaining that they're not able to convert their models. [0]: https://news.ycombinator.com/item?id=26004235 https://news.ycombinator.com/item?id=26004235 [1]: https://github.com/jkjung-avt/tensorrt_demos/blob/master/ssd/build_engine.py https://github.com/jkjung-avt/tensorrt_demos/blob/master/ssd... (In fact, this is the only piece of code I've found on the entire internet that managed to successfully convert my SSD-MobileNetV2.)

dheera 6y ago
They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference 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.
- codethief 6y ago
  > They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference 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/docs/pytorch-ssd.md https://github.com/dusty-nv/jetson-inference/blob/master/doc... [1]: https://github.com/dusty-nv/jetson-inference/issues/896#issuecomment-767033699 https://github.com/dusty-nv/jetson-inference/issues/896#issu... [2]: https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md#downloading-models https://github.com/dusty-nv/jetson-inference/blob/master/doc...

dheera 6y ago

They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference 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.

codethief 6y ago
> They provide some SSD-Mobilenet-v2 here: https://github.com/dusty-nv/jetson-inference 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/docs/pytorch-ssd.md https://github.com/dusty-nv/jetson-inference/blob/master/doc... [1]: https://github.com/dusty-nv/jetson-inference/issues/896#issuecomment-767033699 https://github.com/dusty-nv/jetson-inference/issues/896#issu... [2]: https://github.com/dusty-nv/jetson-inference/blob/master/docs/building-repo-2.md#downloading-models https://github.com/dusty-nv/jetson-inference/blob/master/doc...