Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Deep learning gateway on Raspberry Pi and other edge devices
| Date | Stars |
|---|---|
| 2026-07-24 | 1612 |
| 2026-07-25 | 1612 |
| 2026-07-28 | 1612 |
| 2026-07-30 | 1612 |
| 2026-07-31 | 1611 |
| 2026-08-06 | 1611 |
Today
— stars today
This week
-1 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<p align="center"><a href="http://berrynet.org" target="_blank" rel="noopener noreferrer"><img width="50%" src="https://user-images.githubusercontent.com/292790/33802809-e4726cf8-dd45-11e7-8a64-fdc4c3ff9310.png" alt="BerryNet Logo"></a></p> <h2 align="center">Deep Learning Gateway on Raspberry Pi And Other Edge Devices</h2>  [Supporting BerryNet](https://dt42.io/berrynet-doc/community/donation/) * [One-time donation via PayPal](https://paypal.me/berrynet). * [One-time donation via crypto-currencies](https://dt42.io/berrynet-doc/community/donation/). * [Become a backer or sponsor on Open Collective](https://opencollective.com/berrynet). ## Introduction This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events shown in the images or videos without connecting to the cloud.  <p align="center">Figure 1: BerryNet architecture</p> Figure 1 shows the software architecture of the project, we use Node.js/Python, MQTT and an AI engine to analyze images or video frames with deep learning. So far, there are two default types of AI engines, the classification engine (with Inception v3 [[1]](https://arxiv.org/pdf/1512.00567.pdf) model) and the object detection engine (with TinyYOLO [[2]](https://pjreddie.com/media/files/papers/YOLO9000.pdf) model or MobileNet SSD [[3]](https://arxiv.org/pdf/1704.04861.pdf) model). Figure 2 shows the differences between classification and object detection.  <p align="center">Figure 2: Classification vs detection</p> One of the application of this intelligent gateway is to use the camera to monitor the place you care about. For example, Figure 3 shows the analyzed results from the camera hosted in the DT42 office. The frames were captured by the IP camera and they were submitted into the AI engine. The output from the AI engine will be shown in the dashboard. We are working on the Email and IM notification so you can get a notification when there is a dog coming into the meeting area with the next release.  <p align="center">Figure 3: Object detection result example</p> To bring easy and flexible edge AI experience to user, we keep expending support of the AI engines and the reference HWs.  <p align="center">Figure 4: Reference hardwares</p> ## Installation You can install BerryNet by using pre-built image or from source. Please refer to the [installation guide](https://dt42.io/berrynet-doc/tutorials/installation/) for the details. We are pushing BerryNet into Debian repository, so you will be able to install by only typing one command in the future. Here is the quick steps to install from source: ``` $ git clone https://github.com/DT42/BerryNet.git $ cd BerryNet $ ./configure ``` ## Start and Stop BerryNet BerryNet performs an AIoT application by connecting independent components together. Component types include but not limited to AI engine, I/O processor, data processor (algorithm), or data collector. We recommend to manage BerryNet componetns by [supervisor](http://supervisord.org/), but you can also run BerryNet components
Excerpt of 8,847 characters
Read on GitHub
Bofu Chen · Taiwan
334
35
6
5
3
2
Edward Betts · United Kingdom
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:4ced5886f6f8ed3b, topic:deep-learning, topic:tensorflow
matched fp:4ced5886f6f8ed3b, topic:tensorrt