FastDeploy
jetson-inference
FastDeploy | jetson-inference | |
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5 | 11 | |
2,705 | 7,349 | |
4.3% | - | |
7.5 | 7.7 | |
12 days ago | 11 days ago | |
C++ | C++ | |
Apache License 2.0 | MIT License |
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FastDeploy
- Testing YOLO on Orange Pi 5
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Github-Paddle.js: Run AI models on browsers for computer version.
Refer to this link(https://github.com/PaddlePaddle/FastDeploy/blob/develop/examples/application/js/WebDemo_en.md) for examples and tutorials.
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[P] FastDeploy: Make DL deployment easier and faster!
🔥 2022.10.31:Release FastDeploy release v0.5.0
Repo: https://github.com/PaddlePaddle/FastDeploy
jetson-inference
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Can this NVIDIA Jetson Nano handle advanced machine learning tasks?
Jetson Nano’s are obsolete and no longer supported; but to answer your question, this might be a good place to start.
- help with project involving object detection and tracking with camera
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Jetson Nano 2GB Issues During Training (Out Of Memory / Process Killed) & Other Questions!
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)
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Jetson Nano
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.
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Pretrained image classification model for nuts and bolts (or similar)
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!
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PyTorch 1.8 release with AMD ROCm support
> 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...
- NVIDIA DLSS released as a plugin for Unreal Engine 4
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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 https://github.com/dusty-nv/jetson-inference#hello-ai-world
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I'm tired of this anti-Wayland horseshit
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
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Basic Teaching
https://github.com/dusty-nv/jetson-inference#system-setup
What are some alternatives?
mmdeploy - OpenMMLab Model Deployment Framework
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
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onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
tensorRT_Pro - C++ library based on tensorrt integration
tensorflow - An Open Source Machine Learning Framework for Everyone
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
yolov5-deepsort-tensorrt - A c++ implementation of yolov5 and deepsort
maps-core - The lightweight and modern Map SDK for Android and iOS
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
useful-transformers - Efficient Inference of Transformer models
obs-studio - OBS Studio - Free and open source software for live streaming and screen recording