Caffe has a mixture of command line, Python and Matlab interfaces, you can definitely create a different pipeline that works best for you. To really learn about Caffe, it’s still much better to go through the examples under /caffe/examples/, and to checkout the official documentation, although it’s still not very complete yet. Caffe has command line, Python, and MATLAB interfaces for day-to-day usage, interfacing with research code, and rapid prototyping. While Caffe is a C library at heart and it exposes a modular interface for development, not every occasion calls for custom compilation.
Caffe is a deep learning framework made with expression, speed, and modularity in mind.It is developed by Berkeley AI Research (BAIR) and by community contributors.Yangqing Jia created the project during his PhD at UC Berkeley.Caffe is released under the BSD 2-Clause license.
Python and MATLAB bindings. For rapid proto-typing and interfacing with existing research code, Ca e provides Python and MATLAB bindings. Both languages may be used to construct networks and classify inputs. The Python bindings also expose the solver module for easy pro-totyping of new training procedures. Pre-trained reference models. Caffe networks that take color images as input expect the images to be in BGR format. During import, importCaffeLayers modifies the network so that the imported MATLAB network takes RGB images as input.
Check out our web image classification demo!
Why Caffe?
Expressive architecture encourages application and innovation.Models and optimization are defined by configuration without hard-coding.Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.
Extensible code fosters active development.In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back.Thanks to these contributors the framework tracks the state-of-the-art in both code and models.
Speed makes Caffe perfect for research experiments and industry deployment.Caffe can process over 60M images per day with a single NVIDIA K40 GPU*.That’s 1 ms/image for inference and 4 ms/image for learning and more recent library versions and hardware are faster still.We believe that Caffe is among the fastest convnet implementations available.
Community: Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia.Join our community of brewers on the caffe-users group and Github.
* With the ILSVRC2012-winning SuperVision model and prefetching IO.
Documentation
DIY Deep Learning for Vision with Caffe and Caffe in a Day Tutorial presentation of the framework and a full-day crash course.
Tutorial Documentation Practical guide and framework reference.
arXiv / ACM MM ‘14 paper A 4-page report for the ACM Multimedia Open Source competition (arXiv:1408.5093v1).
Installation instructions Tested on Ubuntu, Red Hat, OS X.
Model Zoo BAIR suggests a standard distribution format for Caffe models, and provides trained models.
Developing & Contributing Guidelines for development and contributing to Caffe.
API Documentation Developer documentation automagically generated from code comments.
Benchmarking Comparison of inference and learning for different networks and GPUs.
Notebook Examples
Image Classification and Filter Visualization Instant recognition with a pre-trained model and a tour of the net interface for visualizing features and parameters layer-by-layer.
Learning LeNet Define, train, and test the classic LeNet with the Python interface.
Fine-tuning for Style Recognition Fine-tune the ImageNet-trained CaffeNet on new data.
Off-the-shelf SGD for classification Use Caffe as a generic SGD optimizer to train logistic regression on non-image HDF5 data.
Multilabel Classification with Python Data Layer Multilabel classification on PASCAL VOC using a Python data layer.
Editing model parameters How to do net surgery and manually change model parameters for custom use.
R-CNN detection Run a pretrained model as a detector in Python.
Siamese network embedding Extracting features and plotting the Siamese network embedding.
Command Line Examples
ImageNet tutorial Train and test 'CaffeNet' on ImageNet data.
LeNet MNIST Tutorial Train and test 'LeNet' on the MNIST handwritten digit data.
CIFAR-10 tutorial Train and test Caffe on CIFAR-10 data.
Fine-tuning for style recognition Fine-tune the ImageNet-trained CaffeNet on the 'Flickr Style' dataset.
Feature extraction with Caffe C++ code. Extract CaffeNet / AlexNet features using the Caffe utility.
CaffeNet C++ Classification example A simple example performing image classification using the low-level C++ API.
Web demo Image classification demo running as a Flask web server.
Siamese Network Tutorial Train and test a siamese network on MNIST data.
Citing Caffe
Please cite Caffe in your publications if it helps your research:
If you do publish a paper where Caffe helped your research, we encourage you to cite the framework for tracking by Google Scholar.
Contacting Us
Join the caffe-users group to ask questions and discuss methods and models. This is where we talk about usage, installation, and applications.
Framework development discussions and thorough bug reports are collected on Issues.
Acknowledgements
The BAIR Caffe developers would like to thank NVIDIA for GPU donation, A9 and Amazon Web Services for a research grant in support of Caffe development and reproducible research in deep learning, and BAIR PI Trevor Darrell for guidance.
The BAIR members who have contributed to Caffe are (alphabetical by first name):Carl Doersch, Eric Tzeng, Evan Shelhamer, Jeff Donahue, Jon Long, Philipp Krähenbühl, Ronghang Hu, Ross Girshick, Sergey Karayev, Sergio Guadarrama, Takuya Narihira, and Yangqing Jia.
The open-source community plays an important and growing role in Caffe’s development.Check out the Github project pulse for recent activity and the contributors for the full list.
We sincerely appreciate your interest and contributions!If you’d like to contribute, please read the developing & contributing guide.
Yangqing would like to give a personal thanks to the NVIDIA Academic program for providing GPUs, Oriol Vinyals for discussions along the journey, and BAIR PI Trevor Darrell for advice.
This software support package provides functions for importing pretrained models as well as layers of Convolutional Neural Networks (CNNs) from Caffe (http://caffe.berkeleyvision.org/). Pretrained models are imported as a SeriesNetwork or a Directed Acyclic Graph (DAG) network object. Autodesk 3ds max 2014 with xforce keygen crack download.
Opening the caffeimporter.mlpkginstall file from your operating system or from within MATLAB will initiate the installation process for the release you have. This mlpkginstall file is functional for R2017a and beyond.
Caffe Matlab Function
Usage Example (importCaffeNetwork): % Specify files to import protofile = 'digitsnet.prototxt'; datafile = 'digits_iter_10000.caffemodel'; % Import network net = importCaffeNetwork(protofile,datafile) Usage Example (importCaffeLayers): % Specify file to import protofile = 'digitsnet.prototxt'; % Import network layers layers = importCaffeLayers('digitsnet.prototxt')
Caffe Matlab Install Ubuntu
For more information on importing Caffe networks, please visit our documentation at https://www.mathworks.com/help/deeplearning/ref/importcaffenetwork.html
Caffe Matlab Interface
For more information on importing layers from Caffe, please visit our documentation at https://www.mathworks.com/help/deeplearning/ref/importcaffelayers.html
Caffe Matlab Tutorial
To get a list of all the pretrained models supported by MATLAB, please visit https://www.mathworks.com/solutions/deep-learning/models.html