sod VS jetson-inference

Compare sod vs jetson-inference and see what are their differences.

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sod jetson-inference
6 11
1,709 7,235
- -
4.8 8.5
5 months ago 16 days ago
C C++
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

sod

Posts with mentions or reviews of sod. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-09-01.

jetson-inference

Posts with mentions or reviews of jetson-inference. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-18.
  • help with project involving object detection and tracking with camera
    2 projects | /r/JetsonNano | 18 Jun 2022
  • PyTorch 1.8 release with AMD ROCm support
    8 projects | news.ycombinator.com | 4 Mar 2021
    > 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...

    8 projects | news.ycombinator.com | 4 Mar 2021
    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.

  • I'm tired of this anti-Wayland horseshit
    16 projects | news.ycombinator.com | 2 Feb 2021
    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

What are some alternatives?

When comparing sod and jetson-inference you can also consider the following projects:

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