https://github.com/1adrianb/face-alignment-training
Training code for the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.
https://github.com/1adrianb/face-alignment-training
deeplearning face-alignment torch7
Last synced: 3 months ago
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Training code for the networks described in "How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper.
- Host: GitHub
- URL: https://github.com/1adrianb/face-alignment-training
- Owner: 1adrianb
- License: other
- Created: 2017-07-13T13:27:10.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2018-01-23T13:01:43.000Z (over 7 years ago)
- Last Synced: 2025-03-25T01:51:10.450Z (3 months ago)
- Topics: deeplearning, face-alignment, torch7
- Language: Lua
- Homepage: https://www.adrianbulat.com/face-alignment
- Size: 15.6 KB
- Stars: 147
- Watchers: 11
- Forks: 46
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
This is the training code for 2D-FAN and 3D-FAN decribed in "How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)" paper. Please visit [our](https://www.adrianbulat.com) webpage or read bellow for instructions on how to run the code.
Pretrained models are available on our page.
**Demo code: **
Note: If you are interested in a binarized version, capable of running on devices with limited resources please also check for a demo.
## Requirments
- Install the latest [Torch7](http://torch.ch/docs/getting-started.html) version (for Windows, please follow the instructions available [here](https://github.com/torch/distro/blob/master/win-files/README.md))
### Packages
- [cutorch](https://github.com/torch/cutorch)
- [nn](https://github.com/torch/nn)
- [nngraph](https://github.com/torch/nngraph)
- [cudnn](https://github.com/soumith/cudnn.torch)
- [xlua](https://github.com/torch/xlua)
- [image](https://github.com/torch/image)
- [paths](https://github.com/torch/paths)
- [matio](https://github.com/soumith/matio-ffi.torch)
## Setup
1. Clone the github repository and install all the dependencies mentiones above.
```bash
git clone https://github.com/1adrianb/face-alignment-training
cd face-alignment-training
```
2. Download the 300W-LP dataset from the authors webpage. In order to train on your own data the dataloader.lua file needs to be adapted.
3. Download the 300W-LP annotations converted to t7 format from [here](https://www.adrianbulat.com/downloads/FaceAlignment/landmarks.zip), extract it and move the ```landmarks``` folder to the root of the 300W-LP dataset.
## Usage
In order to run the demo please download the required models available bellow and the associated data.
```bash
th main.lua -data path_to_300W_LP_dataset
```
In order to see all the available options please run:
```bash
th main.lua --help
```
## Citation
```
@inproceedings{bulat2017far,
title={How far are we from solving the 2D \& 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)},
author={Bulat, Adrian and Tzimiropoulos, Georgios},
booktitle={International Conference on Computer Vision},
year={2017}
}
```
## Acknowledgements
This pipeline is build around the ImageNet training code avaialable at and HourGlass(HG) code available at https://github.com/anewell/pose-hg-train