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Caffe |
Caffe is a framework for convolutional neural network algorithms, developed with speed in mind. It was created by Yangqing Jia as a replacement of decaf, Yangqing's earlier Python implementation of CNNs. It is maintained by the Berkeley Vision and Learning Center and several Berkeley vision group members are actively contributing to the codebase.
Caffe is released under the BSD 2-Clause license.
Decaf, the big brother of Caffe, has a cool demo. Caffe's own demo will come soon.
Caffe aims to provide computer vision scientists with a clean, modifiable implementation of state-of-the-art deep learning algorithms. For example, network structure is easily specified in separate config files, with no mess of hard-coded parameters in the code.
At the same time, Caffe fits industry needs, with blazing fast C++/Cuda code for GPU computation. Caffe is currently the fastest GPU CNN implementation publicly available, and is able to process more than 20 million images per day on a single Tesla K20 machine *.
Caffe also provides seamless switching between CPU and GPU, which allows one to train models with fast GPUs and then deploy them on non-GPU clusters with one line of code: Caffe::set_mode(Caffe::CPU).
Even in CPU mode, computing predictions on an image takes only 20 ms when images are processed in batch mode.
- Presentation: Presentation on Caffe at the UC Berkeley Vision Group meeting.
- Installation: Instructions on installing Caffe (tested on Ubuntu 12.04, but works on Red Hat, OS X, etc.).
- MNIST Demo: example of end-to-end training and testing on the MNIST data.
- Training ImageNet: tutorial on end-to-end training of an ImageNet classifier.
- Running Pretrained ImageNet: simply runs in Python!
- Running Detection: run a pretrained model as a detector.
Please kindly cite Caffe in your publications if it helps your research:
@misc{Jia13caffe,
Author = {Yangqing Jia},
Title = { {Caffe}: An Open Source Convolutional Architecture for Fast Feature Embedding},
Year = {2013},
Howpublished = {\url{http://caffe.berkeleyvision.org/}
}
Yangqing would like to thank the NVidia Academic program for providing a K20 GPU.
The Caffe Matlab wrapper is courtesy of Dr. Ross Girshick.
The detection module (power_wrapper) is courtesy of Sergey Karayev.
Our thanks also go to Jeff Donahue and Oriol Vinyals for various discussions along the journey.
*: When measured with the SuperVision model that won the ImageNet Large Scale Visual Recognition Challenge 2012. More benchmarks coming soon.