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Caffe vs OpenVINO comparison

 

Comparison Buyer's Guide

Executive Summary
 

Categories and Ranking

Caffe
Ranking in AI Development Platforms
21st
Average Rating
7.0
Number of Reviews
1
Ranking in other categories
No ranking in other categories
OpenVINO
Ranking in AI Development Platforms
13th
Average Rating
8.6
Number of Reviews
3
Ranking in other categories
No ranking in other categories
 

Mindshare comparison

As of November 2024, in the AI Development Platforms category, the mindshare of Caffe is 0.2%, down from 0.5% compared to the previous year. The mindshare of OpenVINO is 2.3%, down from 4.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

RL
Speeds up the development process but needs to evolve more to stay relevant
In the future, they should expand text processing, for a recommendation system, or to support some other models as well — that would be great. 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. You can't work with metrics and vectors as Python does. Python is a vector-oriented language, but Caffe is not. When you deal with memory in C++, you have to allocate the data you will use in memory. You have to manage everything in C++. Conversely, in Python, you don't need to do that since everything is abstract and done by Python itself. It depends on every use case or your requirement goals. Some clients will require you to use Caffe because maybe their projects are old and they want to continue with Caffe. Others are comfortable with their current situation or they are afraid of migrating to another library. From my point of view, they need to make it easier for a new developer to use it. They should incorporate Python API to make it richer, overall.
ZM
A free toolkit providing improved neural network performance
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.

Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:
 

Pros

"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 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."
"The initial setup is quite simple."
 

Cons

"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."
 

Pricing and Cost Advice

Information not available
"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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Top Industries

By visitors reading reviews
No data available
Manufacturing Company
47%
University
8%
Computer Software Company
8%
Financial Services Firm
5%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
No data available
 

Comparisons

 

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Overview

Find out what your peers are saying about Microsoft, Google, Amazon Web Services (AWS) and others in AI Development Platforms. Updated: November 2024.
816,406 professionals have used our research since 2012.