RPi_64-bit_Zero-2-image
Raspberry Pi Zero 2 W 64-bit OS image with OpenCV, TensorFlow Lite and ncnn Framework. (by Qengineering)
ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform (by Tencent)
RPi_64-bit_Zero-2-image | ncnn | |
---|---|---|
3 | 12 | |
24 | 19,275 | |
- | 1.2% | |
0.0 | 9.4 | |
about 2 years ago | 3 days ago | |
C++ | ||
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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.
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.
RPi_64-bit_Zero-2-image
Posts with mentions or reviews of RPi_64-bit_Zero-2-image.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-01-02.
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One year on, 64-bit vs 32-bit OS
For Zero 2, with 64-bit you are trading (somewhat) increased performance for available memory. So, really, the choice should depend on what you need more of.
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Building a compact Pi cluster
the 64-bit performance of Bullseye on the Pi Zero 2 W may not be ideal and I may need to rebuild with 32-bit on the nodes
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Install A Raspberry Pi Zero 2 64-bit OS Buster with OpenCV, TensorFlow Lite and ncnn
They helpfully pre-rolled an image on their github with additional instructions here https://github.com/Qengineering/RPi_64-bit_Zero-2-image
ncnn
Posts with mentions or reviews of ncnn.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-02-12.
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
ncnn uses Vulkan for GPU acceleration, I've seen it used in a few projects to get AMD hardware support.
https://github.com/Tencent/ncnn
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[D] Best way to package Pytorch models as a standalone application
They're using NCNN to package the model. Have a look. https://github.com/Tencent/NCNN
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Realtime object detection android app
Hi. Here is my prefered android app for realtime objet detection: https://github.com/nihui/ncnn-android-nanodet ; https://github.com/Tencent/ncnn contains a lot of android demo app for a lot of models.
- ncnn: High-performance neural network inference framework optimized for mobile
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Esp32 tensorflow lite
ncnn home page: https://github.com/Tencent/ncnn
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MMDeploy: Deploy All the Algorithms of OpenMMLab
ncnn
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Draw Things, Stable Diffusion in your pocket, 100% offline and free
Yes, Android devices tend to have bigger RAMs, making running 1024x1024 possible (this is not possible at all on iPhones, which could peak around 5GiB memory with my current implementation, some serious engineering required to bring that down on iPhone devices). The problem is I am not sure about speed. I would likely switch to NCNN (https://github.com/Tencent/ncnn) as the backend which have a decent Vulkan computing kernel support. It is definitely a possibility and there is a path to do that.
- What’s New in TensorFlow 2.10?
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[Technical Article] OCR Upgrade
As the leading open-source inference framework in China and in the world, what we like are its almost zero cost cross-platform capability, high inference speed, and minimal deployment volume. (Project address: https://github.com/Tencent/ncnn)
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Is there a functioning neural netowork or backbone written in pure C language only?
If you’re not planning on training the neural net on an embedded device and just do inference, this might interest you: https://github.com/Tencent/ncnn
What are some alternatives?
When comparing RPi_64-bit_Zero-2-image and ncnn you can also consider the following projects:
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
XNNPACK - High-efficiency floating-point neural network inference operators for mobile, server, and Web