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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 f
by 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...