Skip to content
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians, CVPR 2016 (Spotlight)
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
VOCdevkit add deps Jan 26, 2016
data/mpii_human minor fix Jan 26, 2016
export_fig add deps Jan 26, 2016
matconvnet add deps Jan 26, 2016
.gitignore minor fix Jan 26, 2016
README.md update md Mar 26, 2016
bilinear_u.m a clean version Jan 26, 2016
cnn_get_batch_mpii.m minor fix Jan 26, 2016
cnn_qp1_mpii.m a clean version Jan 26, 2016
cnn_qp2_mpii.m a clean version Jan 26, 2016
cnn_setup_imdb.m a clean version Jan 26, 2016
cnn_test_mpii.m a clean version Jan 26, 2016
cnn_train_dag.m a clean version Jan 26, 2016
dag_viz.m a clean version Jan 26, 2016
initialize_fcn32s.m a clean version Jan 26, 2016
initialize_qp1_fcn16s.m a clean version Jan 26, 2016
initialize_qp1_fcn4s.m a clean version Jan 26, 2016
initialize_qp1_fcn8s.m a clean version Jan 26, 2016
initialize_qp2_fcn16s.m a clean version Jan 26, 2016
initialize_qp2_fcn4s.m a clean version Jan 26, 2016
initialize_qp2_fcn8s.m a clean version Jan 26, 2016
memorySize.m a clean version Jan 26, 2016
run_qp1_mpii.m
run_qp2_mpii.m a clean version Jan 26, 2016
startup.m a clean version Jan 26, 2016

README.md

Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians

Instructions:

  • Compile MatConvNet (If you use CuDNN, make sure you set the $PATH and $LD_LIBRARY_PATH correct)
  • Download the pre-trained vgg-16 model from MatConvNet and put in matconvnet/
  • Download MPII and put in data/mpii_human/
    • see data/mpii_human/README for more details
  • Call run_qp1_mpii.m for training & testing a qp1 model
  • Call run_qp2_mpii.m for training & testing a qp2 model

Pretrained models:

Download our trained models here.

Notes:

A few necessary changes are made based on the original MatConvNet.

See our code and models for face landmark localization on AFLW dataset.

Credits:

The implementation is based on & inspired by MatConvNet and MatConvNet-FCN.

Thanks James for dag_viz.m which dumps a MatConvNet model into a dot file.

You can’t perform that action at this time.