Caffe alternatives and similar packages
Based on the "Deep Learning" category.
Alternatively, view Caffe alternatives based on common mentions on social networks and blogs.
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Pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration -
MXNet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more -
lightning
Deep learning framework to train, deploy, and ship AI products Lightning fast. -
Theano
Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as aesara: www.github.com/pymc-devs/aesara -
Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework. -
Serpent.AI
Game Agent Framework. Helping you create AIs / Bots that learn to play any game you own! -
Silero Models
Silero Models: pre-trained speech-to-text, text-to-speech and text-enhancement models made embarrassingly simple -
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neptune-client
:ledger: The MLOps stack component for experiment tracking -
Spokestack
Spokestack is a library that allows a user to easily incorporate a voice interface into any Python application with a focus on embedded systems.
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README
Caffe
[
](LICENSE)
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.
Check out the project site for all the details like
- DIY Deep Learning for Vision with Caffe
- Tutorial Documentation
- BAIR reference models and the community model zoo
- Installation instructions
and step-by-step examples.
Custom distributions
- Intel Caffe (Optimized for CPU and support for multi-node), in particular Intel® Xeon processors.
- OpenCL Caffe e.g. for AMD or Intel devices.
- Windows Caffe
Community
Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues.
Happy brewing!
License and Citation
Caffe is released under the BSD 2-Clause license. The BAIR/BVLC reference models are released for unrestricted use.
Please cite Caffe in your publications if it helps your research:
@article{jia2014caffe,
Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
Journal = {arXiv preprint arXiv:1408.5093},
Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
Year = {2014}
}
*Note that all licence references and agreements mentioned in the Caffe README section above
are relevant to that project's source code only.