$199.90Original price was: $199.90.$159.90Current price is: $159.90.
NE301 —— Tiny, ultra-efficient on-device AI camera that runs YOLO directly, wakes instantly, and powers real-world vision AI anywhere — modular, battery-powered, and fully open.
No embedded experience needed / Web UI / Few clicks to deploy model
Overview
NeoEyes NE301 is acompact Edge Vision AI Camera built on STMicroelectronics STM32N6 MCU with an integrated NPU.
Delivering 0.6 TOPS compute with only 3 TOPS/W ultraefficiency, it performs on-device image inference such as object recognition, motion detection, and human pose estimation — all without relying on the cloud.
Its open firmware, rich I/O, and modular architecture make NE301 ideal for developers, makers, and researchers building custom vision-AI systems for IoT, robotics, and smart automation.
Most so-called “AI cameras” on the market today are still constrained by a fundamental architectural flaw: they are essentially high-end IP cameras that offload the heavy computational work to distant cloud servers via a simple API. This design inevitably introduces operational bottlenecks — unacceptable latency for real-time applications, excessive bandwidth consumption, and, most critically, serious security concerns. Even the smallest processing tasks require uploading raw and sensitive video streams to an external server.
The strength of this product lies in its ability to run all AI inference, image capture, and processing tasks entirely on-device. This local-first approach significantly enhances personal privacy and data security. What impressed me most, however, is the level of effort put into the documentation. The guides are complete and easy to follow, making it straightforward to get started.
I plan to dig into the source code and explore possibilities for secondary development; training my own AI models also seems like an exciting direction. And by the way — they even provide references for model training, which is genuinely helpful.
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Dave –
Most so-called “AI cameras” on the market today are still constrained by a fundamental architectural flaw: they are essentially high-end IP cameras that offload the heavy computational work to distant cloud servers via a simple API. This design inevitably introduces operational bottlenecks — unacceptable latency for real-time applications, excessive bandwidth consumption, and, most critically, serious security concerns. Even the smallest processing tasks require uploading raw and sensitive video streams to an external server.
The strength of this product lies in its ability to run all AI inference, image capture, and processing tasks entirely on-device. This local-first approach significantly enhances personal privacy and data security. What impressed me most, however, is the level of effort put into the documentation. The guides are complete and easy to follow, making it straightforward to get started.
I plan to dig into the source code and explore possibilities for secondary development; training my own AI models also seems like an exciting direction. And by the way — they even provide references for model training, which is genuinely helpful.