animal_human_kp: A Lua repository from fangd123

Paper

This repository provides code for:

Interspecies Knowledge Transfer for Facial Keypoint Detection. Maheen Rashid, Xiuye Gu, Yong Jae Lee. CVPR 2017.

If you find this repo useful please cite our work:

@inproceedings{rashid2017interspecies,
  title={Interspecies Knowledge Transfer for Facial Keypoint Detection},
  author={Rashid, Maheen and Gu, Xiuye and Lee, Yong Jae},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}

For questions contact Maheen Rashid (mhnrashid at ucdavis dot edu)

Getting Started

Download the code from GitHub:

git clone https://github.com/menoRashid/animal_human_kp
cd animal_human_kp

Install Torch. Instructions are here

Install Torch requirements:

torchx

luarocks install torchx

npy4th (You may need to checkout commit from 5-10-16)

git clone https://github.com/htwaijry/npy4th.git
cd npy4th
luarocks make

Install Python requirements if needed:

Install the Spatial Tranformer module provided:

cd stnbhwd-master
luarocks make

It is a modification of the code from Spatial Transformer Network (Jaderberg et al.) and includes a Thin Plate Spline grid generator layer.

Dataset

Download the Horse Dataset (580 MB)

Run the following commands

cd data
unzip <path to data zip file>

Models

To download all the pretrained and untrained models go here (145 MB)

Run the following commands

cd models
unzip <path to models zip file>

Otherwise add the individual models to models/

Full model for horses with tps warping(36 MB)
Full model for horses with affine warping(63 MB)
TPS Warping model for horses(34 MB)
Affine Warping model for horses(61 MB)
Keypoint network trained on human faces(2.3 MB)
Untrained TPS Warping model(23 MB)
Untrained Affine Warping model(29 MB)

Testing

To test pretrained model run the following commands

cd torch
th test.th -out_dir_images <path to results directory>

after replacing with the path to the folder where you would like the output images to be saved.

A webpage with the results, a text file with the accuracy numbers, and a bar graph, would be in the results directory.

<path to results directory>/results.html
<path to results directory>/stats.txt
<path to results directory>/bar.pdf

Training

The file for training the full model is

torch/train_full_model.th

For details on training run

cd torch
th train_full_model.th -help

To train the model with affine warping uncomment lines 377-378. Currently, all parameters are the parameters used in the paper.

The file for training the warping network is

torch/train_warping_net.th