Caffe

Caffe

BAIR
MatConvNet

MatConvNet

VLFeat
+
+

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About

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. Check out our web image classification demo! 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.

About

The VLFeat open source library implements popular computer vision algorithms specializing in image understanding and local features extraction and matching. Algorithms include Fisher Vector, VLAD, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, quick shift superpixels, large scale SVM training, and many others. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. MatConvNet is a MATLAB toolbox implementing Convolutional Neural Networks (CNNs) for computer vision applications. It is simple, efficient, and can run and learn state-of-the-art CNNs. Many pre-trained CNNs for image classification, segmentation, face recognition, and text detection are available.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Audience

Anyone looking for an open-source deep learning framework with expression, speed and modularity

Audience

Anyone in need of a deep learning software

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

API

Offers API Supported

API

Offers API Not Supported

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version Supported
Free Trial Not Supported

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

BAIR
United States
caffe.berkeleyvision.org

Company Information

VLFeat
United States
www.vlfeat.org/matconvnet/

Alternatives

MXNet

MXNet

The Apache Software Foundation

Alternatives

DeepSpeed

DeepSpeed

Microsoft
DataMelt

DataMelt

jWork.ORG
LiveLink for MATLAB

LiveLink for MATLAB

Comsol Group
MATLAB

MATLAB

The MathWorks

Categories

AI Development Supported
Deep Learning Supported
Neural Network Supported

Categories

Deep Learning Supported

Deep Learning Features

Convolutional Neural Networks Not Supported
Document Classification Supported
Image Segmentation Not Supported
ML Algorithm Library Not Supported
Model Training Not Supported
Neural Network Modeling Not Supported
Self-Learning Not Supported
Visualization Supported

Deep Learning Features

Convolutional Neural Networks Supported
Document Classification Supported
Image Segmentation Supported
ML Algorithm Library Not Supported
Model Training Not Supported
Neural Network Modeling Not Supported
Self-Learning Not Supported
Visualization Not Supported

Integrations

AWS Elastic Fabric Adapter (EFA) Supported
AWS Marketplace Supported
Amazon Web Services (AWS) Supported
Docker Supported
Fabric for Deep Learning (FfDL) Supported
Lambda Supported
NVIDIA DIGITS Supported
OpenVINO Supported
Polyaxon Supported
Pop!_OS Supported
Zebra by Mipsology Supported

Integrations

AWS Elastic Fabric Adapter (EFA) Not Supported
AWS Marketplace Not Supported
Amazon Web Services (AWS) Not Supported
Docker Not Supported
Fabric for Deep Learning (FfDL) Not Supported
Lambda Not Supported
NVIDIA DIGITS Not Supported
OpenVINO Not Supported
Polyaxon Not Supported
Pop!_OS Not Supported
Zebra by Mipsology Not Supported
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