https://github.com/UCBAIR/decaf-release

Decaf is DEPRECATED! Please visit http://caffe.berkeleyvision.org/ for Caffe, the new framework that has all the good things: GPU computation, full train/test scripts, native C++, and an active community!
https://github.com/UCBAIR/decaf-release

Last synced: 17 days ago
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Decaf is DEPRECATED! Please visit http://caffe.berkeleyvision.org/ for Caffe, the new framework that has all the good things: GPU computation, full train/test scripts, native C++, and an active community!

• Host: GitHub
• URL: https://github.com/UCBAIR/decaf-release
• Owner: UCBAIR
• License: other
• Created: 2013-10-21T00:23:01.000Z (about 11 years ago)
• Default Branch: master
• Last Pushed: 2015-12-19T06:53:31.000Z (almost 9 years ago)
• Last Synced: 2024-04-10T11:13:57.091Z (7 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 10.3 MB
• Stars: 232
• Watchers: 40
• Forks: 120
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
Decaf

=====

Decaf is a framework that implements convolutional neural networks, with the

goal of being efficient and flexible. It allows one to easily construct a

network in the form of an arbitrary Directed Acyclic Graph (DAG) and to

perform end-to-end training.

For more usage check out [the wiki](https://github.com/UCB-ICSI-Vision-Group/decaf-release/wiki).

A great place to start is running [ImageNet classification on an image](https://github.com/UCB-ICSI-Vision-Group/decaf-release/wiki/imagenet).

For the pre-trained imagenet DeCAF feature and its analysis, please see our

[technical report on arXiv](http://arxiv.org/abs/1310.1531). Please consider

citing our paper if you use Decaf in your research:

    @article{donahue2013decaf,

      title={DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition},

      author={Donahue, Jeff and Jia, Yangqing and Vinyals, Oriol and Hoffman, Judy and Zhang, Ning and Tzeng, Eric and Darrell, Trevor},

      journal={arXiv preprint arXiv:1310.1531},

      year={2013}

    }

For Anaconda users experiencing libm error: it is because anaconda ships with a libm.so binary that does not support GLIBC_2.15, which gets loaded earlier than the system libm. You can fix this error by you can replacing anaconda's libm file with a newer version.