We performed a comparison between Caffe and OpenVINO based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Google, TensorFlow and others in AI Development Platforms."Caffe has helped our company become up-to-date in the market and has helped us speed up the development process of our projects."
"The inferencing and processing capabilities are quite beneficial for our requirements."
"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 concept of Caffe is a little bit complex because it was developed and based in C++. They need to make it easier for a new developer, data scientist, or a new machine or deep learning engineer to understand it."
"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."
"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."
Caffe is ranked 17th in AI Development Platforms while OpenVINO is ranked 11th in AI Development Platforms. Caffe is rated 7.0, while OpenVINO is rated 8.6. The top reviewer of Caffe writes "Speeds up the development process but needs to evolve more to stay relevant". On the other hand, the top reviewer of OpenVINO writes "A free toolkit providing improved neural network performance". Caffe is most compared with PyTorch, whereas OpenVINO is most compared with TensorFlow, PyTorch, Azure OpenAI, Google Cloud AI Platform and Google Vertex AI.
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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.
