Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework that supports a variety of deep learning architectures such as CNN, RCNN, LSTM and fully connected networks. With its graphics processing unit (GPU) support and out-of-the-box templates that simplify model setup and training, Caffe is most popular for image classification and segmentation tasks.
In Caffe, you can define model, solver and optimization details in configuration files, thanks to its expressive architecture. In addition, you can switch between GPU and central processing unit (CPU) computation by changing a single flag in the configuration file. Together, these features eliminate the need for hard coding in your project, which is normally required in other deep learning frameworks. Caffe is also considered one of the fastest convolutional network implementations available.
Is Caffe Still Used?
Caffe Applications
Caffe is used in a wide range of scientific research projects, startup prototypes and large-scale industrial applications in natural language processing, computer vision and multimedia. Several projects are built on top of the Caffe framework, such as Caffe2 and CaffeOnSpark. Caffe2 is built on Caffe and is merged into Meta’s PyTorch. Yahoo has also integrated Caffe with Apache Spark to create CaffeOnSpark, which brings deep learning to Hadoop and Spark clusters.
How Does Caffe Work?
Interfaces
Caffe is primarily a C++ library and exposes a modular development interface, but not every situation requires custom compilation. Therefore, Caffe offers interfaces for daily use by way of the command line, Python and MATLAB.
Data Processing
Caffe processes data in the form of Blobs which are N-dimensional arrays stored in a C-contiguous fashion. Data is stored both as data we pass along the model and as diff, which is a gradient computed by the network.
Data layers handle how the data is processed in and out of the Caffe model. Pre-processing and transformation like random cropping, mirroring, scaling and mean subtraction can be done by configuring the data layer. Furthermore, pre-fetching and multiple-input configurations are also possible.
Caffe Layers
Caffe layers and their parameters are the foundation of every Caffe deep learning model. The bottom connection of the layer is where the input data is supplied and the top connection is where the results are provided after computation. In each layer, three different computations take place, which are setup, forward and backward computations. In that respect, they are also the primary unit of computation.
Many state-of-the-art deep learning models can be created with Caffe using its layer catalog. Data layers, normalization layers, utility layers, activation layers and loss layers are among the layer types provided by Caffe.
Caffe Solver
Caffe solver is responsible for learning — specifically for model optimization and generating parameter updates to improve the loss. There are several solvers provided in Caffe such as stochastic gradient descent, adaptive gradient and RMSprop. The solver is configured separately to decouple modeling and optimization.
What Are the Pros and Cons of Using Caffe?
Pros of Caffe
Caffe Is Fast
- Caffe is one of the fastest convolutional network implementations available.
- Caffe can process over 60 million images per day with a single NVIDIA K40 GPU with pre-fetching. That’s one millisecond per image for inference and four millisecond per image for learning.
Caffe Is Easy to Use
- No coding is required for most of the cases. Mode, solver and optimization details can be defined in configuration files.
- There are ready-to-use templates for common use cases.
- Caffe supports GPU training.
- Caffe is an open-source framework.
Cons of Caffe
Caffe Is Not Flexible
- A new network layer must be coded in C++/Cuda.
- It is difficult to experience new deep learning architectures not already covered in Caffe.
- HDF5 is the only output format. Additionally, the framework only supports a few input formats.
- Integration with other deep learning frameworks is limited.
- Defining the models in configuration files can be challenging when the model parameters and layer numbers increase.
- There’s no high-level API to speed up the initial development.
Caffe Has a Limited Community and Little Commercial Support
- Caffe is developing at a slow pace. As a result, its popularity among machine learning professionals is diminishing.
- The documentation is limited and most support is provided by the community alone, rather than the developer.
- The absence of commercial support discourages enterprise-grade developers.
What Are Caffe Alternatives?
In addition to Caffe, there are many other deep learning frameworks available, such as:
- TensorFlow by Google: With its huge and active community, it is the most famous end-to-end machine learning platform which provides tools not only for deep learning but also for statistical and mathematical calculations.
- Keras by François Chollet: This is a high-level API for developing deep neural networks, while using TensorFlow as a back-end engine. It's easy to create sophisticated neural models with Keras, thanks to its simple programming interface.
- PyTorch by Meta: PyTorch is another open-source machine learning framework used for applications such as computer vision and natural language processing.
- Apache MxNet by Apache Software Foundation: This is an open-source deep learning framework that allows you to develop deep learning projects on a wide range of devices. Apache MxNet supports multiple languages such as Python, C++, R, Scala, Julia, MATLAB and JavaScript.
History of Caffe
Yangqing Jia initiated the Caffe project during his doctoral studies at the University of California, Berkeley. Caffe was developed (and is currently maintained) by Berkeley AI Research and community contributors under the BSD license. It is written in C++ and its first stable release date was April 18, 2017.
