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

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

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.