/StructuredFeature

This is the code for our work "Structured Feature Learning for Pose estimation"

Primary LanguageJupyter Notebook

Structured Feature Learning for Pose Estimation

This is the code for our work Structured Feature Learning for Pose estimation

Training

Make caffe: We write our own layer for loss, channel dropout and mix interpolation, if you are not going to use these functions, you can use your own caffe.

make matcaffe

Get LMDB: Run "Data_prepare.m" in matlab to generate LMDB requires Train the caffe model: Run "Baseline.sh. You may need the pre-train fully convolutional VGG-16 model.

./Baseline.sh

Test: Select the best model for testing, and run "TestModel.m" to see the results.

Released models

We provide a model we trained on LSP dataset (itration = 3250). If you are going to test this model, please download it and put it in the location specified in code, and set the variable "test_our_provided_model" to true.

Cite

If you use this code, please cite our work

@inproceedings{chu2016structure, 
title={Structured Feature Learning for Pose Estimation}, 
author={Chu, Xiao and Ouyang, Wanli and Li,Hongsheng and Wang, Xiaogang}, 
booktitle={CVPR}, year={2016} 
}

Our project is written based on Xianjie Chen's NIPS2014