I used OpenVINO for almost three years, starting in 2020. My initial use case involved attempting to connect or run models using quantization within cameras as edge devices instead of processing all input frames on a server. This was aimed at performing surveillance analysis within the home, like fire detection or human fall detection. View full review »

I am a computer vision developer who uses OpenVINO to build video analytics systems for commercial purposes. View full review »

We currently make technology that uses the Intel VPU, the Movidius chipset. We run OpenVINO on it.

It's for Edge IoT. We make the hardware and we cater to customers who are looking for Edge IoT solutions, and the product is really for edge processing or video co-processing for machine vision. We distribute that data on the customer's network using our Edge solution, which is based on DDS, distributed data system. Basically, we use it for machine vision applications.

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I created a retail recognition custom model — a model on RPX 2017. Afterward, I transferred it to OpenVINO for object detection and retail detection.

We have a team of three people who use OpenVINO.

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OpenVINO is good for budgets because you don't have a computer vision model for classification for object detection obligations. You can run it on a server with Azure but it can be costly. Sometimes the application has to be on heavy dedicated hardware, like a small computer. In this case, machine learning applications are not so good because they demand a lot of computer resources and a lot of CPU resources are not so fast. In terms of accuracy and speed trade-off performance, you have to sacrifice a bit of accuracy in your inference in order to get better speed. When you deploy models with the OpenVINO format into devices like PC boards, it's a great tool. The online testing platform that Intel has for OpenVINO is really nice.

It's sort of like a sandbox environment. You want to test what kind of hardware is available. You can test it and watch what works better in talking about the preference. Later you can decide based on the budget.

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