Average Ratings 0 Ratings
Average Ratings 0 Ratings
Description
Caffe is a deep learning framework designed with a focus on expressiveness, efficiency, and modularity, developed by Berkeley AI Research (BAIR) alongside numerous community contributors. The project was initiated by Yangqing Jia during his doctoral studies at UC Berkeley and is available under the BSD 2-Clause license. For those interested, there is an engaging web image classification demo available for viewing! The framework’s expressive architecture promotes innovation and application development. Users can define models and optimizations through configuration files without the need for hard-coded elements. By simply toggling a flag, users can seamlessly switch between CPU and GPU, allowing for training on powerful GPU machines followed by deployment on standard clusters or mobile devices. The extensible nature of Caffe's codebase supports ongoing development and enhancement. In its inaugural year, Caffe was forked by more than 1,000 developers, who contributed numerous significant changes back to the project. Thanks to these community contributions, the framework remains at the forefront of state-of-the-art code and models. Caffe's speed makes it an ideal choice for both research experiments and industrial applications, with the capability to process upwards of 60 million images daily using a single NVIDIA K40 GPU, demonstrating its robustness and efficacy in handling large-scale tasks. This performance ensures that users can rely on Caffe for both experimentation and deployment in various scenarios.
Description
Founded in January 2015, sipXcom emerged from a fork of the sipXecs project, initiated by the eZuce, Inc. development team. The sipXecs and SIPfoundry communities were struggling to expand their developer base due to a limiting contributor agreement, prompting the creation of sipXcom, which features an open contribution model. The source code for sipXcom is licensed under the copyleft-friendly AGLP v3 (Affero General Public License), ensuring greater flexibility for developers. Currently under development, SWARM is the codename for the upcoming iteration of the sipXcom and sipXecs projects, with a production launch expected in early 2017. This new platform will adopt a microservices-based architecture, enhancing scalability, reliability, and configurability beyond what sipX currently offers. SWARM is engineered to be compatible with any computing environment, including dedicated, virtual, or cloud servers, and also allows for hybrid setups that merge on-premise and cloud solutions. With these advancements, sipXcom aims to significantly improve user experience and functionality in the ever-evolving communication landscape.
API Access
Has API
Yes
API Access
Has API
No
Integrations
Amazon Web Services (AWS)
Yes
AWS Elastic Fabric Adapter (EFA)
Yes
AWS Marketplace
Yes
Docker
Yes
Fabric for Deep Learning (FfDL)
Yes
IBM Cloud
No
Lambda
Yes
NVIDIA DIGITS
Yes
OpenVINO
Yes
Polyaxon
Yes
Integrations
Amazon Web Services (AWS)
Yes
AWS Elastic Fabric Adapter (EFA)
No
AWS Marketplace
No
Docker
No
Fabric for Deep Learning (FfDL)
No
IBM Cloud
Yes
Lambda
No
NVIDIA DIGITS
No
OpenVINO
No
Polyaxon
No
Pricing Details
No price information available.
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
No
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
Yes
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
Yes
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
BAIR
Country
United States
Website
caffe.berkeleyvision.org
Vendor Details
Company Name
sipXcom
Founded
2015
Country
United States
Website
sipxcom.org
Product Features
Deep Learning
Convolutional Neural Networks
No
Document Classification
Yes
Image Segmentation
No
ML Algorithm Library
No
Model Training
No
Neural Network Modeling
No
Self-Learning
No
Visualization
Yes