A brief glance at both Caffe and Caffe2 will reveal numerous similarities.
Both are open source deep learning frameworks with a Python interface and a leaning towards image classification and segmentation.
However, the differences between Caffe and Caffe2 lie in the basic function of the software.
Switching layers for operators, Caffe2 became more flexible, more modular, and, ultimately, more useful.
In this guide, we’ll take a look at both Caffe and Caffe2. We’ll explore how they compare, the key differences, and which framework (if any) is right for you.
What Is Caffe?
Developed at the University of California, Berkeley, Caffe is an open source deep learning framework.
Hosted on GitHub, Caffe (aka Convolutional Architecture for Fast Feature Embedding), supports various deep learning capabilities. It’s primarily aimed at image classification and image segmentation.
Caffe, sometimes referred to as Caffe 1.0, has been used for both academic research and product development.
It supports fully-connected neural network designs as well as CNN, LSTM, and GPU- and CPU- based acceleration computational kernel libraries.
The expressive architecture of Caffe had numerous applications, with an extensible code that has been picked over and examined by more than 1000 developers.
Quick and efficient, Caffe quickly made a name for itself in the field of deep learning.
However, the relevance of Caffe 1.0 faded fast with the release of Caffe2.
Do Companies Still Use Caffe?
Yes, companies do still use Caffe, although it isn’t hugely common. It’s been several years since the initial release of Caffe, and in that time, deep learning has grown almost exponentially.
However, while Caffe had some uses in research programs, it has been largely used in the production and development of products.
Some companies that have found Caffe useful continued using the software, rather than struggling to translate models to a more advanced program.
What Is Caffe2?
Released not long after Caffe hit the world, Caffe2 is another machine learning tool with an open source and a leaning towards image classification.
Designed for development and production applications, Caffe2 has proved popular with machine learning enthusiasts as well as corporate developers.
Caffe2 offers a more flexible and modular approach to machine learning.
Members of the machine learning community contributed new models to Caffe2, giving it increased capacity for experimentation.
Using interchangeable native Python and C++ APIs, users could quickly create and organize prototypes with Caffe2.
With the original Caffe framework acting as a solid base, the new and improved Caffe2 was optimized from top to toe.
Highly flexible and increasingly modular, the Caffe2 framework acted almost as if it wasn’t a framework, vastly improving the range of applications.
Caffe2 And PyTorch
In 2018, it was announced that deep learning platforms Caffe2 and PyTorch would be merged.
The merge happened relatively seamlessly, moving the source code of Caffe2 to PyTorch. This was designed to increase and extend the functionality of both platforms.
This merging did less to disrupt the use of Caffe2 than you might imagine. Cross-compilation build modes and graph construction programs for Caffe2 were still available.
Caffe2 also stayed supported on numerous platforms.
The primary effect of the merging was that development on Caffe2 came to a halt. Instead, development moved forward with PyTorch.
While some considered the loss of the individual Caffe2 program to be disappointing, the merging of frameworks did make it easier for developers new to deep learning.
Differences Between Caffe And Caffe2
Caffe and Caffe2 are built on a similar framework and with a similar purpose. Both are open source deep learning frameworks, and both were developed by Yangqing Jia.
However, it’s perhaps unsurprising to discover that Caffe2 was developed to build on and surpass the original Caffe framework.
With an eye toward flexibility and scalability, Caffe2 introduced the use of operators.
The operators are a new take on the layers featured in the original Caffe.
More than 400 operators were included in the initial Caffe2 framework, allowing the community to guide and grow the resource for better use.
Caffe2 also includes mobile and scale deployments, alongside support for large-scale distributed training.
The overall effect of the update was that Caffe2 had better longevity and was prepared to take on vast applications, including those on the scale of Facebook.
Caffe was created to deal with conventional CNN applications. The lack of versatility wasn’t necessarily a major issue, but it did mean the framework lacked a certain ability to grow.
Which Is Better: Caffe Or Caffe2
Undoubtedly, Caffe2 is the better deep learning framework.
As it comes from Caffe, it has all the exceptional deep learning functionality of the original, with improved modularity, scalability, and application.
Caffe was created as a Ph.D. project, with an original use case of standard CNN applications.
While Caffe offered an unparalleled performance under these conditions, it was limited when faced with new computation patterns.
Ultimately, Caffe is incredibly useful in a certain set of circumstances. However, these circumstances are limited.
The original build of Caffe can’t keep up with various new use cases.
While some companies and individual users can (and do) find continuing uses for Caffe, its usefulness has largely been usurped by Caffe2.
The increased modularity of Caffe2, as well as the mobile and scale developments, makes it more flexible. This ensures better longevity and a wider potential application.
And by introducing operators, Caffe2 became an exciting resource for deep learning enthusiasts and developers.
The many uses of Caffe2 meant it was later merged into PyTorch. This move was to reduce overheads for Python users and streamline the Facebook deep learning frameworks.
Caffe and Caffe2 are both deep learning frameworks with a focus on image classification and a production-focused application.
Designed at the University of California, the open source Caffe framework proved immediately popular with machine learning enthusiasts.
However, Caffe had definite limitations. Caffe2 replaced the layers of Caffe with flexible operators, allowing for a modular application. It could be used in more ways, by more people, and for longer.
While Caffe2 drew from Caffe, it surpassed the original on release. And thanks to easy tools allowing you to move Caffe models to Caffe2, Caffe has largely fallen out of use.
Frequently Asked Questions
Built from the same basic framework, Caffe2 and Caffe are both deep learning tools with a primary focus on image classification and segmentation.
While Caffe excelled within conventional CNN applications, it lacked diversity and flexibility.
Caffe2 introduced a range of operators that allowed the framework to be developed further, for better modularity and both scale and mobile uses.
Yes, for the most part, you can directly convert Caffe models to Caffe2. Caffe2 provides a tutorial and a translator that allows you to convert your models from Caffe 1.0 to Caffe2.
However, Caffe did produce some models that can’t be easily converted to Caffe2 using the Caffe2 provided translator.
This can typically be solved with the use of a custom script. The script will need to stuff layers in blobs before you label them and continue with the model.
Caffe2 and PyTorch were merged in 2018. This was primarily motivated by Facebook and intended to reduce overhead for the Python user community.
While previously Facebook operated with two deep learning frameworks, merging Caffe2 into PyTorch streamlined the system.
- Who Is Hubspot Marketing Hub Biggest Competitors & Alternatives? - August 28, 2023
- Why Is AI Booming In The Workplace At The Moment? - August 21, 2023
- Why TensorFlow Is So Popular (Here Are 5 Reasons Why) - August 7, 2023