Caffe

Caffe

BAIR
+
+

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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

Steev is an AI training assistant that manages your training runs, eliminating the need for constant supervision and enhancing model performance. It reviews and analyzes your code before training begins, identifying potential errors, providing fixes, and suggesting better approaches to improve your workflow and outcomes. Steev takes proactive steps, adjusts training parameters, and resolves issues before they escalate, going beyond mere observation. Tracking all key variables during training, Steev sends instant notifications when your attention is needed, eliminating constant progress checks. Everything you need for smarter training is built into Steev, ready to go with zero setup required. You can try Steev for free during their beta program.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

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

Audience

Machine learning engineers seeking an automated solution to optimize and monitor their model training processes, reducing manual oversight and improving efficiency

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

No information available.
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

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
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

BAIR
United States
caffe.berkeleyvision.org

Company Information

Steev
United States
www.steev.io

Alternatives

MXNet

MXNet

The Apache Software Foundation

Alternatives

DeepSpeed

DeepSpeed

Microsoft
DeepSpeed

DeepSpeed

Microsoft
Tune Studio

Tune Studio

NimbleBox

Categories

Categories

Deep Learning Features

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Integrations

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

Integrations

AWS Elastic Fabric Adapter (EFA)
AWS Marketplace
Amazon Web Services (AWS)
Docker
Fabric for Deep Learning (FfDL)
Lambda
NVIDIA DIGITS
OpenVINO
Polyaxon
Pop!_OS
Python
Zebra by Mipsology
Claim Caffe and update features and information
Claim Caffe and update features and information
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