Caffe
What it is
Caffe is a free deep learning framework developed by Berkeley AI Research and community contributors. It lets users define models and optimization through configuration instead of hard-coded implementation, with command-line, Python, and MATLAB interfaces. The framework runs locally on CPUs or GPUs; GPU mode requires CUDA, and NVIDIA cuDNN can provide acceleration. CPU-only builds are also supported. The Caffe Model Zoo supplies a format and tools for sharing model information and downloading trained model binaries. Listed model formats are prototxt and caffemodel. Described applications include visual classification, image similarity, speech, robotics, and other tasks. Caffe supports local training, on-premises deployment, and distributed training. Installation guidance covers Ubuntu, Debian, OS X, RHEL, CentOS, Fedora, Windows, Docker, and AWS setups. Make is officially supported for builds, while CMake is community supported. The project is released under the BSD 2-Clause license and describes use in academic research, startup prototypes, and industrial applications.
For and against
For
- Free under the BSD 2-Clause license
- Supports CPU and GPU operation
- Provides Python and MATLAB interfaces
- Includes a model-sharing and download toolset
- Supports distributed training
Against
- GPU mode requires CUDA
- CMake support is community supported
- No free trial is offered
- Builds require local setup
The verdict
Caffe provides a configurable framework with broad platform and interface coverage, plus CPU, GPU, and distributed-training options. Its free license suits development and research, while installation and acceleration depend on the user’s environment.
Who it suits
Caffe suits researchers, startups, and industrial teams building deep learning systems with configurable models and local or on-premises training. It is useful for developers working in C++, Python, or MATLAB.
Plans and prices
Caffe
BSD 2-Clause licensed deep learning framework
Read 8 Oct 2026 · caffe.berkeleyvision.org
On the record
- Acceleration
- Caffe can use NVIDIA cuDNN for GPU acceleration and can also be built in CPU-only mode.caffe.berkeleyvision.org · read 8 Oct 2026
- Audience
- The project describes use across academic research, startup prototypes, and industrial applications.caffe.berkeleyvision.org · read 8 Oct 2026
- Builds
- The installation guide says Make is officially supported and CMake is community supported.caffe.berkeleyvision.org · read 8 Oct 2026
- Compute
- Caffe supports CPU and GPU operation, with GPU mode requiring CUDA.caffe.berkeleyvision.org · read 8 Oct 2026
- Design
- Caffe emphasizes expression, speed, modularity, openness, and community.caffe.berkeleyvision.org · read 8 Oct 2026
- Interfaces
- Caffe provides command-line, Python, and MATLAB interfaces.caffe.berkeleyvision.org · read 8 Oct 2026
- License
- Caffe is released under the BSD 2-Clause license.caffe.berkeleyvision.org · read 8 Oct 2026
- Model configuration
- Models and optimization can be defined with configuration rather than hard-coded.caffe.berkeleyvision.org · read 8 Oct 2026
- Models
- The Caffe Model Zoo provides a format and tools for sharing model information and downloading trained model binaries.caffe.berkeleyvision.org · read 8 Oct 2026
- Purpose
- Caffe is a deep learning framework developed by Berkeley AI Research and community contributors.caffe.berkeleyvision.org · read 8 Oct 2026
- Support
- The site directs usage and installation questions to the caffe-users group and bug reports to GitHub Issues.caffe.berkeleyvision.org · read 8 Oct 2026
- Supported platforms
- The installation guide lists Ubuntu, Debian, OS X, RHEL, CentOS, Fedora, Windows, Docker, and AWS setup options.caffe.berkeleyvision.org · read 8 Oct 2026
- Use cases
- The site describes Caffe models for visual classification, image similarity, speech, robotics, and other tasks.caffe.berkeleyvision.org · read 8 Oct 2026