EAST
A tensorflow implementation of EAST text detector (by argman)
ncnn
ncnn is a high-performance neural network inference framework optimized for the mobile platform (by Tencent)
| EAST | ncnn | |
|---|---|---|
| 1 | 12 | |
| 2,993 | 19,234 | |
| - | 1.0% | |
| 0.0 | 9.4 | |
| over 1 year ago | 6 days ago | |
| C++ | C++ | |
| GNU General Public License v3.0 only | 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.
EAST
Posts with mentions or reviews of EAST.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-12-03.
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How the network architecture in EAST text detector translates to keras?
I'm not sure whether I'm among a few or many who struggle translating academic papers to code. I tried looking for EAST reliable implementation, and so far I only found this (TF 1.x), this (pytorch), this (pytorch), ... and a few others. The problem is that I cannot tell the differences between each and the paper's implementation. Here's the network architecture below, can someone explain how I should read and interpret / convert what is understood from the figure to a keras model. The parts I find tricky:
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