Caffe for multi-label classification
NOTE: A modified version of Caffe master branch, including a multilabel image data layer, LSE pooling layer, weighted sigmoid cross entropy loss layer and multilabel accuracy layer.
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by 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
- BVLC reference models and the community model zoo
- Installation instructions
and step-by-step examples.
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 BVLC reference models are released for unrestricted use.
Please cite our CVPR paper in your publications if it helps your research:
@InProceedings{wang2017chestxray,
author = {Wang, Xiaosong and Peng, Yifan and Lu, Le and Lu, Zhiyong and Bagheri, Mohammadhadi and Summers, Ronald},
title = {ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
booktitle = {2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)},
pages = {3462--3471},
year = {2017}
}