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jetson-inference
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sod | jetson-inference | |
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6 | 11 | |
1,709 | 7,235 | |
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4.8 | 8.5 | |
5 months ago | 16 days ago | |
C | C++ | |
GNU General Public License v3.0 or later | MIT License |
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sod
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Ask HN: Who is hiring? (September 2023)
PixLab (https://pixlab.io) & FACEIO (https://faceio.net) | Full-or-part-time | Remote | Computer Vision / Full stack Engineers |
PixLab, a leading provider of Machine Vision, Face Recognition & Media Processing APIs is looking for:
* Embedded C & Computer Vision engineer(s) to work on the SOD (https://sod.pixlab.io), embedded computer vision library.
* Senior Python engineer with proficiency in PyTorch to work on FACEIO (https://faceio.net), our facial authentication web framework for web sites & apps.
* C++ developer with ML expertise to work on the port of Tiny-Dream (https://pixlab.io/tiny-dream), our embedded Stable Diffusion C++ library from ncnn to ggml.
* React/Vue JS Web developer(s) with expertise in fabric.js to work on a brand new, web based photo editing software backed by generative AI.
Reach out to Vincent via contact AT pixlab.io with your resume if interested.
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Show HN: VisionScript, abstract programming language for computer vision
Shameless plug: take a look to our embedded computer vision library SOD: https://sod.pixlab.io.
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Raku Blog Posts 2023.28
Khalid Elborai elaborates on their experiences integrating the SOD library into a Raku module.
- Modern Image Processing Algorithms Implementation in C
jetson-inference
- help with project involving object detection and tracking with camera
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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...
They provide some SSD-Mobilenet-v2 here:
https://github.com/dusty-nv/jetson-inference
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.
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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.
What are some alternatives?
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
tensorflow - An Open Source Machine Learning Framework for Everyone
yolov5-deepsort-tensorrt - A c++ implementation of yolov5 and deepsort
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
obs-studio - OBS Studio - Free and open source software for live streaming and screen recording
trt_pose_hand - Real-time hand pose estimation and gesture classification using TensorRT
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
YOLOv4-Tiny-in-UnityCG-HLSL - A modern object detector inside fragment shaders
sway - i3-compatible Wayland compositor
tensorrt_demos - TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet
darknet_ros - YOLO ROS: Real-Time Object Detection for ROS