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OpenVINO Room for Improvement

Mahender Reddy Pokala - PeerSpot reviewer
AI Developer at University of Chicago
One improvement could be making OpenVINO less dependent on Intel-based processing chips. Expanding cross-platform compatibility, allowing it to work beyond Intel and its edge devices, would be beneficial. It should support different hardware platforms with certain requirements rather than being hardware-dependent. View full review »
DS
Computer Vision Engineer at Ivideon
It would be great if OpenVINO could convert new models into its format more quickly. The PyTorch model hub has more models compared to the OpenVINO model hub. It would also be beneficial if the support for Apple silicon was improved. View full review »
reviewer1530384 - PeerSpot reviewer
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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Buyer's Guide
OpenVINO
June 2025
Learn what your peers think about OpenVINO. Get advice and tips from experienced pros sharing their opinions. Updated: June 2025.
860,825 professionals have used our research since 2012.
ZM
Machine Learning Software Developer at freelancer

The model optimization is a little bit slow — it could be improved. They should introduce some type of deep learning accelerator, like Jetson Xavier NX.

There is a lacking in vehicle recognition — types of vehicles. Differentiating between cars, SUVs and different types of light, heavy, and medium trucks can be tricky. We have to train such models ourselves and then transfer them onto OpenVINO.

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CR
Freelance Engineer at Autónomo

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. These are the main disadvantages that OpenVINO has that are a bit limited for some models.

View full review »
Buyer's Guide
OpenVINO
June 2025
Learn what your peers think about OpenVINO. Get advice and tips from experienced pros sharing their opinions. Updated: June 2025.
860,825 professionals have used our research since 2012.