Systems and Solutions Architect at a tech services company with 1,001-5,000 employees
Real User
2021-03-17T10:39:19Z
Mar 17, 2021
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.
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.
I used OpenVINO for my deep learning solutions. I was working on some traffic management problems and had to detect pedestrians, cars, and some accidents via traffic videos. So, I used OpenVINO to implement my deep learning solutions to some servers. The OpenVINO software was on the local machine where we use the software. I used the OpenVINO hardware Mustang-V100-MX8, which includes eight Myriad Cores. I used OpenVINO software with these cards. We had two people focusing on OpenVINO. My colleague was dealing with communication about the project. He was communicating with Intel and other project partners about our problems, and I was focusing on the implementation of the system.
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.
OpenVINO toolkit quickly deploys applications and solutions that emulate human vision. Based on Convolutional Neural Networks (CNNs), the toolkit extends computer vision (CV) workloads across Intel hardware, maximizing performance. The OpenVINO toolkit includes the Deep Learning Deployment Toolkit (DLDT).
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.
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.
I used OpenVINO for my deep learning solutions. I was working on some traffic management problems and had to detect pedestrians, cars, and some accidents via traffic videos. So, I used OpenVINO to implement my deep learning solutions to some servers. The OpenVINO software was on the local machine where we use the software. I used the OpenVINO hardware Mustang-V100-MX8, which includes eight Myriad Cores. I used OpenVINO software with these cards. We had two people focusing on OpenVINO. My colleague was dealing with communication about the project. He was communicating with Intel and other project partners about our problems, and I was focusing on the implementation of the system.
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.