Caffe - Deep learning framework by BAIR with expression, speed, and modularity

Caffe Deep learning framework by BAIR with expression, speed, and modularity

Free

#Deep Learning
#computer vision
#neural networks
#Machine Learning
#gpu computing
#Research

What is Caffe?

Developed by Berkeley AI Research (BAIR) and community contributors, Caffe stands as a powerful deep learning framework that prioritizes expression, speed, and modularity. The framework offers configuration-based model development, allowing users to define and optimize without hard-coding, while providing seamless switching between CPU and GPU processing.

With remarkable performance capabilities, Caffe can process over 60M images per day using a single NVIDIA K40 GPU, achieving processing speeds of 1ms/image for inference and 4ms/image for learning. The framework supports various applications ranging from academic research to large-scale industrial implementations in vision, speech, and multimedia.

Features

GPU/CPU Processing: Single-flag switching between processing modes
High Performance: Processes 60M+ images daily on NVIDIA K40 GPU
Modular Architecture: Configuration-based model definition without hard-coding
Extensible Framework: Active development and community contributions
Model Zoo: Standard distribution format for pre-trained models
Multi-platform Support: Compatible with Ubuntu, Red Hat, and OS X

Use Cases

Academic research projects
Computer vision applications
Speech processing systems
Multimedia analysis
Industrial-scale machine learning deployment
Image classification and recognition
Neural network training and deployment

FAQs

What platforms does Caffe support?

Caffe is tested and supported on Ubuntu, Red Hat, and OS X operating systems.
How fast is Caffe's image processing capability?

Using a single NVIDIA K40 GPU, Caffe can process over 60M images per day, with 1ms/image for inference and 4ms/image for learning.
Is Caffe open source?

Yes, Caffe is released under the BSD 2-Clause license and is open source.