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 »

What could be improved in OpenVINO is making the product more cross-platform. I know they are working with third-party plugins to extend the toolkit, and in this way, I can use it with NVIDIA GPUs or with other hardware because now it's primarily working in all Intel hardware. CPU, GPUs, TPUs, but only from Intel. If they make more cross-platform functionality, it would be great. It's difficult to make it work faster than the NVIDIA toolkit in their own GPUs. At least having the possibility and making it work faster than now in other hardware that is not from Intel provided would be beneficial.

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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 »

I have heard good things about OpenVINO. It doesn't consume much current for external GPU usage. However, it has some downsides because I couldn't get it to run on my Raspberry Pi 4.

While not specifically for my use case, I would be happy if the software packages were available for the Intel Neural Compute Stick 2. It is actually a really good product, but I couldn't get it to run on my Raspberry Pi 4 because the software packages to download were no longer available. They have been deleted. A wish would be to maintain the software packages so that even after five or ten years, they would still be available to download and install.

It would benefit me because it would run the model much faster than without using OpenVINO to boost the GPU performance.

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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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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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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.

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