BVLC/caffe

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

34556 stars · 18423 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Caffe is a fast, modular deep learning framework developed by Berkeley AI Research, focused on convolutional neural networks for vision, speech, and multimedia. Models are defined via plaintext protobuf configuration, with C++ core and Python/MATLAB/command-line interfaces, and CPU/GPU switching via a single flag.

Use cases

• train convolutional neural networks for image classification
• deploy fast CNN inference on GPU or CPU
• run pretrained vision models from the model zoo
• train siamese networks with contrastive loss
• process millions of images per day for vision research
• define neural network architectures without writing code

When to choose

• you need a proven, fast CNN framework for computer vision research or deployment
• you want config-file-defined models with CPU/GPU portability
• you need to reproduce classic vision models like AlexNet or GoogLeNet
• you work in C++ and want an extensible layer system

When to avoid

• you need modern architectures like transformers or dynamic graphs - use PyTorch or TensorFlow instead
• you need active development and current community support
• you require Python 3-first tooling or modern ecosystem integrations
• you are starting a new deep learning project today

Facets

framework · maturity maintenance

deep-learning machine-learning computer-vision image-processing gpu-computing deep-learning machine-learning computer-vision image-processing windows cpp python cross-platform convolutional-networks cuda cudnn model-zoo bsd-license legacy-framework linux macos docker gpu

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