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OpenVINO vs PyTorch comparison

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OpenVINO Logo
3,494 views|2,324 comparisons
PyTorch Logo
887 views|589 comparisons
Featured Review
Buyer's Guide
OpenVINO vs. PyTorch
July 2022
Find out what your peers are saying about OpenVINO vs. PyTorch and other solutions. Updated: July 2022.
621,593 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The initial setup is quite simple.""The features for model comparison, the feature for model testing, evaluation, and deployment are very nice. It can work almost with all the models.""The inferencing and processing capabilities are quite beneficial for our requirements."

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"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code.""Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."

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Cons
"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.""The model optimization is a little bit slow — it could be improved.""At this point, the product could probably just use a greater integration with more machine learning model tools."

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"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice.""There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."

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Pricing and Cost Advice
  • "We didn't have to pay for any licensing with Intel OpenVINO. Everything is available on their site and easily downloadable for free."
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  • "It is free."
  • "PyTorch is an open-source solution."
  • More PyTorch Pricing and Cost Advice →

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    Questions from the Community
    Top Answer:The inferencing and processing capabilities are quite beneficial for our requirements.
    Top Answer:We didn't have to pay anything for Intel OpenVINO, everything was available on their site. All of their solutions, inference engines, and other model optimizations are all available for free. We… more »
    Top Answer: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 —… more »
    Top Answer:I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code.
    Ranking
    3rd
    Views
    3,494
    Comparisons
    2,324
    Reviews
    3
    Average Words per Review
    731
    Rating
    8.7
    4th
    Views
    887
    Comparisons
    589
    Reviews
    2
    Average Words per Review
    462
    Rating
    9.0
    Comparisons
    Learn More
    OpenVINO
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    PyTorch
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    Overview

    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've built this course as an introduction to deep learning. Deep learning is a field of machine learning utilizing massive neural networks, massive datasets, and accelerated computing on GPUs. Many of the advancements we've seen in AI recently are due to the power of deep learning. This revolution is impacting a wide range of industries already with applications such as personal voice assistants, medical imaging, automated vehicles, video game AI, and more.

    In this course, we'll be covering the concepts behind deep learning and how to build deep learning models using PyTorch. We've included a lot of hands-on exercises so by the end of the course, you'll be defining and training your own state-of-the-art deep learning models.

    Offer
    Learn more about OpenVINO
    Learn more about PyTorch
    Top Industries
    VISITORS READING REVIEWS
    Manufacturing Company28%
    Comms Service Provider20%
    Computer Software Company15%
    Educational Organization5%
    VISITORS READING REVIEWS
    Comms Service Provider19%
    Manufacturing Company14%
    Computer Software Company14%
    Educational Organization10%
    Company Size
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise14%
    Large Enterprise72%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise10%
    Large Enterprise75%
    Buyer's Guide
    OpenVINO vs. PyTorch
    July 2022
    Find out what your peers are saying about OpenVINO vs. PyTorch and other solutions. Updated: July 2022.
    621,593 professionals have used our research since 2012.

    OpenVINO is ranked 3rd in AI Development Platforms with 3 reviews while PyTorch is ranked 4th in AI Development Platforms with 2 reviews. OpenVINO is rated 8.6, while PyTorch is rated 9.0. The top reviewer of OpenVINO writes "Open-source, easy to integrate, and perfectly tailored to the Movidius chipset". On the other hand, the top reviewer of PyTorch writes "A highly user-friendly open-source machine learning library". OpenVINO is most compared with TensorFlow, Google Cloud AI Platform, Microsoft Azure Machine Learning Studio and Caffe, whereas PyTorch is most compared with Microsoft Azure Machine Learning Studio, Caffe, IBM Watson Machine Learning, TensorFlow and MXNet. See our OpenVINO vs. PyTorch report.

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    We monitor all AI Development Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.