1adrianb / binary-human-pose-estimation

This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos.

Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

This code implements a demo of the Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources paper by Adrian Bulat and Georgios Tzimiropoulos.

[2021 Update]: PyTorch repo with training code for BNN available here: https://github.com/1adrianb/binary-networks-pytorch

For the Face Alignment demo please check: https://github.com/1adrianb/binary-face-alignment

Requirements

• Install the latest Torch7 version (for Windows, please follow the instructions avaialable here)

Packages

• cutorch
• nn
• cudnn (cudnn5 preffered)
• xlua
• image
• gnuplot
• cURL
• paths

Setup

Clone the github repository

git clone https://github.com/1adrianb/binary-human-pose-estimation --recursive
cd binary-human-pose-estimation

Build and install the BinaryConvolution package

cd bnn.torch/; luarocks make; cd ..;

Install the modified optnet package

cd optimize-net/; luarocks make rocks/optnet-scm-1.rockspec; cd ..;

Run the following command to prepare the files required by the demo. This will download 10 images from the MPII dataset alongside the dataset structure converted to .t7

th download-content.lua

Download the model available bellow and place it in the models folder.

Usage

In order to run the demo simply type:

th main.lua

Pretrained models

Layer type	Model Size	MPII error
MPII	1.3MB	76.0

Note: More pretrained models will be added soon

Notes

For more details/questions please visit the project page or send an email at [e-mail]