- Clone from the official caffe, will continuely be up to date by the official caffe code
- Add faster rcnn joint train and test [DONE]
- Add action recognition [DONE]
- Add spatial transform network [Not Started]
- Speed up[DOING]
- With demos including all tasks above ^_^
The official Faster R-CNN code (written in MATLAB) is available here. If your goal is to reproduce the results in our NIPS 2015 paper, please use the official code.
This repository contains a C++ reimplementation of the Python code(py-faster-rcnn). This C++ implementation is built on the offcial caffe, I will continue to update this code for improvement and up-to-date by offcial caffe.
All following steps, you should do these in the $CAFFE_ROOT path.
Using sh example/FRCNN/demo.sh, the will process five pictures in the examples/FRCNN/images , and put results into examples/FRCNN/results .
Note: You should prepare the trained caffemodel into models/FRCNN/ as ZF_faster_rcnn_final.caffemodel
Using sh example/FRCNN/train.sh, the will start train voc2007 data using ZF model.
- VOCdevkit should be put into $CAFFE_ROOT, ** ln -s $YOUR_VOCdevkit_Path $CAFFE_ROOT/VOCdevkit
- ZF pretrain model should be put into models/FRCNN/ as ZF.v2.caffemodel
More details in the code.
- git remote add caffe https://github.com/BVLC/caffe.git
- git fetch caffe
- git rebase caffe/master
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!
Caffe is released under the BSD 2-Clause license. The 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}
}