Best Deep Learning Software for OpenVINO
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Compare the Top Deep Learning Software that integrates with OpenVINO as of June 2025
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This a list of Deep Learning software that integrates with OpenVINO. Use the filters on the left to add additional filters for products that have integrations with OpenVINO. View the products that work with OpenVINO in the table below.
What is Deep Learning Software for OpenVINO?
Deep learning software provides tools and frameworks for developing, training, and deploying artificial neural networks, particularly for complex tasks such as image and speech recognition, natural language processing (NLP), and autonomous systems. These platforms leverage large datasets and powerful computational resources to enable machines to learn patterns and make predictions. Popular deep learning software includes frameworks like TensorFlow, PyTorch, Keras, and Caffe, which offer pre-built models, libraries, and tools for designing custom models. Deep learning software is essential for industries that require advanced AI solutions, including healthcare, finance, automotive, and entertainment. Compare and read user reviews of the best Deep Learning software for OpenVINO currently available using the table below. This list is updated regularly.
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Keras
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API. -
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Caffe
BAIR
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. -
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PaddlePaddle
PaddlePaddle
PaddlePaddle is based on Baidu's years of deep learning technology research and business applications and integrates deep learning core framework, basic model library, end-to-end development kit, tool components and service platform. It was officially open-sourced in 2016 and is a comprehensive An industry-level deep learning platform with open source, leading technology, and complete functions. The flying paddle is derived from industrial practice and has always been committed to in-depth integration with the industry. At present, flying paddles have been widely used in industry, agriculture, and service industries, serving 3.2 million developers, and working with partners to help more and more industries complete AI empowerment.
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