OpenVINO Reviews

Name: OpenVINO
Brand: Intel
Rating: 4 (3 reviews)

Vendor: Intel

4.3 out of 5

3 reviews
100% willing to recommend

What is OpenVINO?

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).

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OpenVINO is the #13 ranked solution in top AI Development Platforms. PeerSpot users give OpenVINO an average rating of 8.6 out of 10. OpenVINO is most commonly compared to Microsoft Azure Machine Learning Studio: OpenVINO vs Microsoft Azure Machine Learning Studio. OpenVINO is popular among the large enterprise segment, accounting for 78% of users researching this solution on PeerSpot. The top industry researching this solution are professionals from a manufacturing company, accounting for 47% of all views.

Helped 814,763 peers since 2012

Featured reviews

Zafar Muhammed

Machine Learning Software Developer at freelancer

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 It's a great solution for getting camera images, processing them, and extracting the reserves. It's…

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reviewer1530384

Systems and Solutions Architect at a tech services company with 1,001-5,000 employees

Generally, when you deploy edge products, it's really about latency. It's about getting that camera input, being able to process it, extracting the information you need, and getting the solution back to the person who made the request. Although I'm not necessarily saying its latency or accuracy is bad, it's always something that can be improved upon. By focusing on improving these areas, they can make the overall solution even better. At this point, the product could probably just use a greater integration with more machine learning model tools. However, that's not advice from experience per se. That's always just helpful in general. To be able to incorporate more models into the product makes it stronger. Therefore, to be clear, it's not coming from a point of a current deficiency. It's just a general comment.

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ChristianRivera

Freelance Engineer at Autónomo

Install the latest version that already has fixes for old problems. Work with some neural network, with a few layers to test it. If you use a neural network, like a fast R-CNN, it wouldn't work because it's too complex and there are some layers that are not recognized by OpenVINO. Start small and continue growing. Make an account in the Intel OpenVINO platform so you can start testing with Jupyter Notebook and send your inference jobs to different kinds of devices. Check the performance, know the differences between the different hardware, and how you can site the project. It is a good platform. The price I have seen is not so expensive. That's my advice. OpenVINO is a good tool if you want to work not in the cloud, you have to work on the edge. If you want to adapt your models, it has a good pipeline to follow. You have to learn it well because it could take time to debug some of the errors. Some of them are not very explained and you have to go through your codes a bit blind. I would rate it a nine out of ten because it's a very good tool. It's not complete but is the best in the market right now.

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OpenVINO mindshare

As of November 2024, the mindshare of OpenVINO in the AI Development Platforms category stands at 2.3%, down from 4.7% compared to the previous year, according to calculations based on PeerSpot user engagement data.

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Key learnings from peers

Valuable Features

"The initial setup is quite simple."
"The inferencing and processing capabilities are quite beneficial for our requirements."
"The features for model comparison, the feature for model testing, evaluation, and deployment are very nice. It can work almost with all the models."

Room for Improvement

"At this point, the product could probably just use a greater integration with more machine learning model tools."
"The model optimization is a little bit slow — it could be improved."
"It has some disadvantages because when you're working with very complex models, neural networks if OpenVINO cannot convert them automatically and you have to do a custom layer and later add it to the model. It is difficult."