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

Disco.dev is an open source personal hub for MCP (Model Context Protocol) integration that lets users discover, launch, customize, and remix MCP servers with zero setup, no infrastructure overhead required. It provides plug‑and‑play connectors and a collaborative environment where users can spin up servers instantly via CLI or local execution, explore and remix community‑shared servers, and tailor them to unique workflows. This streamlined, infrastructure‑free approach accelerates AI automation development, democratizes access to agentic tooling, and fosters open collaboration across technical and non-technical contributors through a modular, remixable ecosystem.

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

Developers and AI innovators interested in a solution to experiment with and tailor MCP servers

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

Free
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

Disco.dev
United States
disco.dev/

Alternatives

MXNet

MXNet

The Apache Software Foundation

Alternatives

DeepSpeed

DeepSpeed

Microsoft
Remix

Remix

Remix Framework

Categories

Categories

Deep Learning Features

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

Integrations

Amazon Web Services (AWS)
1Password
AWS Elastic Fabric Adapter (EFA)
Attio
Deel
Disco
Docker
GitHub
Linear
Logfire
Model Context Protocol (MCP)
OpenVINO
Parallel AI
Perplexity
Pylon
Replicate
Supabase
Todoist
Xero

Integrations

Amazon Web Services (AWS)
1Password
AWS Elastic Fabric Adapter (EFA)
Attio
Deel
Disco
Docker
GitHub
Linear
Logfire
Model Context Protocol (MCP)
OpenVINO
Parallel AI
Perplexity
Pylon
Replicate
Supabase
Todoist
Xero
Claim Caffe and update features and information
Claim Caffe and update features and information
Claim Disco.dev and update features and information
Claim Disco.dev and update features and information