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Caffe vs PyTorch 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
PyTorch
Ranking in AI Development Platforms
8th
Average Rating
8.6
Number of Reviews
10
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 PyTorch is 1.5%, down from 2.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.
AI Development Platforms
 

Featured Reviews

RL
Nov 24, 2020
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.
Arucy Lionel - PeerSpot reviewer
Nov 27, 2023
Offers good backward compatible and simple to use
We work a lot with text processing, vectorization, and other NLP tasks. Sometimes, we need to process websites, presentations, or optics quickly because they're used in user engines and other applications. We use PyTorch to test our implementations as well One of the things I really like about…

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 product's initial setup phase is easy."
"It's been pretty scalable in terms of using multiple GPUs."
"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."
"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."
"It’s reliable, secure and user-friendly. It allows you to develop any AIML project efficiently. PySearch is the best option for developing any project in the AIML domain. The product is easy to install."
"For me, the product's initial setup phase is easy...For beginners, it is fairly easy to learn."
"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."
"We use PyTorch libraries, which are working well. It's very easy."
 

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."
"The training of the models could be faster."
"The product has breakdowns when we change the versions a lot."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
"The product has certain shortcomings in the automation of machine learning."
"I would like to see better learning documents."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"On the production side of things, having more frameworks would be helpful."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
 

Pricing and Cost Advice

Information not available
"PyTorch is an open-source solution."
"PyTorch is open source."
"The solution is affordable."
"It is free."
"It is free."
"PyTorch is open-sourced."
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Top Industries

By visitors reading reviews
No data available
Manufacturing Company
29%
Computer Software Company
11%
Healthcare Company
8%
University
7%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

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What needs improvement with PyTorch?
We faced an issue with PyTorch due to version incompatibility. PyTorch has no latest version after v12.3. We also faced a few version compatibility issues with CUDA drivers.
 

Comparisons

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Overview

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814,763 professionals have used our research since 2012.