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
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
Download the model available bellow and place it in the models folder.
In order to run the demo simply type:
|Layer type||Model Size||MPII error|
Note: More pretrained models will be added soon