Caffe
Original author(s)	Yangqing Jia
Developer(s)	Berkeley Vision and Learning Center

Stable release	1.0^[1] / 18 April 2017; 6 years ago (2017-04-18)

Repository	github.com/BVLC/caffe
Written in	C++
Operating system	Linux, macOS, Windows^[2]
Type	Library for deep learning
License	BSD^[3]
Website	caffe.berkeleyvision.org

Machine learning and data mining
Part of a series on
Paradigms Supervised learning Unsupervised learning Online learning Batch learning Meta-learning Semi-supervised learning Self-supervised learning Reinforcement learning Rule-based learning Quantum machine learning
Problems Classification Generative model Regression Clustering dimension reduction density estimation Anomaly detection Data Cleaning AutoML Association rules Semantic analysis Structured prediction Feature engineering Feature learning Learning to rank Grammar induction Ontology learning Multimodal learning
Supervised learning (classification • regression) Apprenticeship learning Decision trees Ensembles Bagging Boosting Random forest k-NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine (RVM) Support vector machine (SVM)
Clustering BIRCH CURE Hierarchical k-means Fuzzy Expectation–maximization (EM) DBSCAN OPTICS Mean shift
Dimensionality reduction Factor analysis CCA ICA LDA NMF PCA PGD t-SNE SDL
Structured prediction Graphical models Bayes net Conditional random field Hidden Markov
Anomaly detection RANSAC k-NN Local outlier factor Isolation forest
Artificial neural network Autoencoder Cognitive computing Deep learning DeepDream Feedforward neural network Recurrent neural network LSTM GRU ESN reservoir computing Restricted Boltzmann machine GAN Diffusion model SOM Convolutional neural network U-Net Transformer Vision Mamba Spiking neural network Memtransistor Electrochemical RAM (ECRAM)
Reinforcement learning Q-learning SARSA Temporal difference (TD) Multi-agent Self-play
Learning with humans Active learning Crowdsourcing Human-in-the-loop
Model diagnostics Coefficient of determination Confusion matrix Learning curve ROC curve
Mathematical foundations Kernel machines Bias–variance tradeoff Computational learning theory Empirical risk minimization Occam learning PAC learning Statistical learning VC theory
Machine-learning venues ECML PKDD NeurIPS ICML ICLR IJCAI ML JMLR
Related articles Glossary of artificial intelligence List of datasets for machine-learning research List of datasets in computer vision and image processing Outline of machine learning
v t e

Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license.^[4] It is written in C++, with a Python interface.^[5]

History

Yangqing Jia created the Caffe project during his PhD at UC Berkeley.^[6] It is currently hosted on GitHub.^[7]

Features

Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs.^[8] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL.^[9]^[10]

Applications

Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.^[11]

Caffe2

In April 2017, Facebook announced Caffe2,^[12] which included new features such as recurrent neural network (RNN). At the end of March 2018, Caffe2 was merged into PyTorch.^[13]

References

External links

Deep learning software

Deep learning software
Open source	Apache MXNet Apache SINGA Caffe Deeplearning4j DeepSpeed Dlib Keras Microsoft Cognitive Toolkit ML.NET OpenNN PyTorch TensorFlow Theano Torch ONNX OpenVINO
Proprietary	Apple Core ML IBM Watson Neural Designer Wolfram Mathematica MATLAB Deep Learning Toolbox
Category Comparison