Caffe was developed by Yangging Jia while he was pursuing a Ph.D. at the University of California, Berkeley. Caffe has become popular and it has wide support from a community working in academia, start-ups, large enterprises, media, and others. 

One of its big supporters is Yahoo. They integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.
This combination of products provides them with tremendous processing speed, making it ideal for image processing. In 2017, Facebook launched Caffe2 with features like Recurrent Neural Networks. Eventually, Caffe2 was merged into PyTorch in 2018.

Project Background

  • Framework: Caffe 
  • Author: Yangqing Jia
  • Released: April 2017
  • Type: Open-source library for deep learning
  • License: BDS
  • Supports: CPUs, GPUs, Nvidia CUDA, and cuDNN Library (CUDA Deep Neural Network)
  • Language: C++ and Python
  • GitHub: BVLC/caffe
  • Runs on: Mac OS, Windows, Linux, Ubuntu, Red Hat, and OS X


  • Classification
  • Regression
  • Clustering
  • Dimensionality reduction
  • Structured prediction
  • Anomaly detection
  • Reinforcement learning
  • Artificial neural network
  • Image classification
  • Image segmentation
  • CNN, RCNN, and LSTM
  • Fully connected neural network designs
  • Large-scale industrial applications in vision, speech, and multimedia


  • Supports image classification and image segmentation
  • Supports CPU (Intel MKL) and GPU (cuDNN)
  • Supports Region-based convolutional networks (R-CNN) and Faster R-CNN
  • Popular in academic research projects, prototypes, and large-scale applications
  • Facebook released Caffe2 but in March 2018, it was rolled into PyTorch
  • Allows users to switch from GPU to CPU by the setting of a single flag – first, it trains on GPU, then deploys on commodity servers
  • High-performance image processing that can process 60M images per day on one Nvidia K40 GPU
  • Provides model definitions, pre-trained weights, and optimization setting for quick starts 
  • Features include denoising, depth estimation, optical flow, an semantic segmentation

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