CaffeBAIR
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Related Products
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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.
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About
DeepCube focuses on the research and development of deep learning technologies that result in improved real-world deployment of AI systems. The company’s numerous patented innovations include methods for faster and more accurate training of deep learning models and drastically improved inference performance. DeepCube’s proprietary framework can be deployed on top of any existing hardware in both datacenters and edge devices, resulting in over 10x speed improvement and memory reduction. DeepCube provides the only technology that allows efficient deployment of deep learning models on intelligent edge devices. After the deep learning training phase, the resulting model typically requires huge amounts of processing and consumes lots of memory. Due to the significant amount of memory and processing requirements, today’s deep learning deployments are limited mostly to the cloud.
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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
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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
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Audience
Anyone looking for an open-source deep learning framework with expression, speed and modularity
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Audience
Professionals interested in a solution to make the training of deep learning models faster
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Not Supported
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
No information available.
Free Version
Supported
Free Trial
Not Supported
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Pricing
No information available.
Free Version
Not Supported
Free Trial
Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Training
Documentation
Supported
Webinars
Supported
Live Online
Supported
In Person
Supported
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Company InformationBAIR
United States
caffe.berkeleyvision.org
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Company InformationDeepCube
Israel
www.deepcube.com/technology/
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Alternatives |
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Categories |
Categories |
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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
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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
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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
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