caffe

Caffe official website, one of the popular deep learning frameworks

What is Caffe?

Caffe is a clear and efficient deep learning framework with a pure C++/CUDA architecture, supporting command line, Python, and MATLAB interfaces, and can seamlessly switch between CPU and GPU.

Caffe official website:https://caffe.berkeleyvision.org/

Caffe project address:https://github.com/BVLC/caffe

Main advantages of Caffe

(1) Seamless switching between CPU and GPU;

(2) Models and optimizations are set through configuration files, without code;

Caffe framework was born in 2013, written in C++, providing MATLAB and Python language interfaces, with clear interfaces and is one of the popular deep learning frameworks.

Caffe has excellent convolutional neural network models, defining the entire network layer by layer from data input to output, consisting of standard array structures Blob, basic units Layer Layers, connected sets Net, and solution methods Solver. Although Caffe is particularly suitable for machine vision, its performance on recurrent neural networks is generally average. Caffe2 adds support for natural language processing, time series prediction, and support for RNN, LSTM.

The advantages of the Caffe framework include:

• Easy to get started. Network models, parameters, and optimization methods are stored in text form, and new models can be obtained by simply rewriting the network configuration file.
• Fast image processing speed Caffe is not only suitable for processing massive image data but also very fast.
• Highly modular. New network models can be defined according to rules and extended to other models or anything.
• Free and open-source contributes to the continuous improvement of Caffe's performance.

Data structures in Caffe:

Mastering the data structures in Caffe is the basic condition for applying Caffe, and the format of the data structure blob for image data, convolutional neural network convolutional weights, and biases is specified as follows:

(1) Image data: number × channel number × height × width.

(2) Convolutional neural network weights: number of convolutional kernels × convolutional kernel channel number × convolutional kernel height × convolutional kernel width.

(3) Convolutional neural network bias: number of biases × 1 × 1 × 1.

After 2019, Caffe 1 and 2 were surpassed by PyTorch, which became the most popular deep learning framework, followed by TensorFlow. In fact, a dual champion situation of PyTorch + TensorFlow has formed.

In theory, Caffe 2 still has a speed advantage in inference deployment runtime (although the code writing speed is 1 order of magnitude slower, as it is too difficult to use). However, Caffe author Yangqing Jia left fb in 2019 to join Alibaba, leading Alibaba's open-source publicity work, which means he also left the front-line development of Caffe. The original Caffe development team within Facebook also gradually transferred their work to PyTorch, and Caffe 3 is far away.