Skip to content
Source code release of the paper: Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation.
MATLAB Python Shell
Branch: master
Clone or download

Latest commit

Fetching latest commit…
Cannot retrieve the latest commit at this time.

Files

Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
GNet-caffe @ cfe6c77
dataset
testing
training
.gitattributes
.gitignore
.gitmodules
LICENSE.md
README.md
get_dataset.sh
get_models.sh
get_preds.sh

README.md

GNet-pose

Project Page: http://guanghan.info/projects/guided-fractal/

UPDATE 9/27/2018:

Prototxts and model that achieved 93.9Pck on LSP dataset. http://guanghan.info/download/Data/GNet_update.zip

When I was replying e-mails, it occurred to me that the models that I had uploaded was around May/June 2017 (performance in old arxiv version), and in August 2017 the performance was improved to 93.9 on LSP with a newer caffe version which fixed the downsampling and/or upsampling deprecation problem (Yeah, it "magically" improved the performance). The best model was 94.0071 on LSP dataset, but it was not uploaded nor published on the benchmark.


Overview

Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation.

Source code release of the paper for reproduction of experimental results, and to aid researchers in future research.


Prerequisites


Getting Started

1. Download Data and Pre-trained Models

  • Datasets (MPII [1], LSP [2])

    bash ./get_dataset.sh
    
  • Models

    bash ./get_models.sh
    
  • Predictions (optional)

    bash ./get_preds.sh
    

2. Testing

  • Generate cropped patches from the dataset for testing:

    cd testing/
    matlab gen_cropped_LSP_test_images.m
    matlab gen_cropped_MPII_test_images.m
    cd -
    

    This will generate images with 368-by-368 resolution.

  • Reproduce the results with the pre-trained model:

    cd testing/
    python .test.py
    cd -
    

    You can choose different dataset to test on, with different models. You can also choose different settings in test.py, e.g., with or without flipping, scaling, cross-heatmap regression, etc.

3. Training

  • Generate Annotations

    cd training/Annotations/
    matlab MPI.m LEEDS.m
    cd -
    

    This will generate annotations in json files.

  • Generate LMDB

    python ./training/Data/genLMDB.py
    

    This will load images from dataset and annotations from json files, and generate lmdb files for caffe training.

  • Generate Prototxt files (optional)

    python ./training/GNet/scripts/gen_GNet.py
    python ./training/GNet/scripts/gen_fractal.py
    python ./training/GNet/scripts/gen_hourglass.py
    
  • Training:

     bash ./training/train.sh
    

4. Performance Evaluation

cd testing/eval_LSP/; matlab test_evaluation_lsp.m; cd../

cd testing/eval_MPII/; matlab test_evaluation_mpii_test.m

5. Results

More Qualitative results can be found in the project page. Quantitative results please refer to the arxiv paper.


License

GNet-pose is released under the Apache License Version 2.0 (refer to the LICENSE file for details).


Citation

If you use the code and models, please cite the following paper: TMM 2017.

@article{ning2017knowledge, 
 author={G. Ning and Z. Zhang and Z. He}, 
     journal={IEEE Transactions on Multimedia}, 
     title={Knowledge-Guided Deep Fractal Neural Networks for Human Pose Estimation}, 
     year={2017}, 
     doi={10.1109/TMM.2017.2762010}, 
     ISSN={1520-9210}, }

Reference

[1] Andriluka M, Pishchulin L, Gehler P, et al. "2d human pose estimation: New benchmark and state of the art analysis." CVPR (2014).

[2] Sam Johnson and Mark Everingham. "Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation." BMVC (2010).

You can’t perform that action at this time.