{"id":20268985,"url":"https://github.com/1adrianb/face-alignment-training","last_synced_at":"2025-04-11T03:51:04.137Z","repository":{"id":71161920,"uuid":"97125466","full_name":"1adrianb/face-alignment-training","owner":"1adrianb","description":"Training code for the networks described in \"How far are we from solving the 2D \u0026 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)\" paper.","archived":false,"fork":false,"pushed_at":"2018-01-23T13:01:43.000Z","size":16,"stargazers_count":147,"open_issues_count":9,"forks_count":46,"subscribers_count":11,"default_branch":"master","last_synced_at":"2025-03-25T01:51:10.450Z","etag":null,"topics":["deeplearning","face-alignment","torch7"],"latest_commit_sha":null,"homepage":"https://www.adrianbulat.com/face-alignment","language":"Lua","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/1adrianb.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-07-13T13:27:10.000Z","updated_at":"2025-02-19T07:14:20.000Z","dependencies_parsed_at":"2023-02-22T12:15:22.222Z","dependency_job_id":null,"html_url":"https://github.com/1adrianb/face-alignment-training","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/1adrianb%2Fface-alignment-training","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/1adrianb%2Fface-alignment-training/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/1adrianb%2Fface-alignment-training/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/1adrianb%2Fface-alignment-training/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/1adrianb","download_url":"https://codeload.github.com/1adrianb/face-alignment-training/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248339262,"owners_count":21087214,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deeplearning","face-alignment","torch7"],"created_at":"2024-11-14T12:22:21.609Z","updated_at":"2025-04-11T03:51:04.120Z","avatar_url":"https://github.com/1adrianb.png","language":"Lua","readme":"# How far are we from solving the 2D \\\u0026 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)\n\nThis is the training code for 2D-FAN and 3D-FAN decribed in \"How far are we from solving the 2D \\\u0026 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.\n\nPretrained models are available on our page.\n\n**Demo code: \u003chttps://www.github.com/1adrianb/2D-and-3D-face-alignment\u003e**\n\nNote: If you are interested in a binarized version, capable of running on devices with limited resources please also check \u003chttps://github.com/1adrianb/binary-face-alignment\u003e for a demo.\n\n## Requirments\n\n- 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))\n\n### Packages\n\n- [cutorch](https://github.com/torch/cutorch)\n- [nn](https://github.com/torch/nn)\n- [nngraph](https://github.com/torch/nngraph)\n- [cudnn](https://github.com/soumith/cudnn.torch)\n- [xlua](https://github.com/torch/xlua)\n- [image](https://github.com/torch/image)\n- [paths](https://github.com/torch/paths)\n- [matio](https://github.com/soumith/matio-ffi.torch)\n\n## Setup\n\n1. Clone the github repository and install all the dependencies mentiones above.\n\n```bash\n\ngit  clone https://github.com/1adrianb/face-alignment-training\ncd face-alignment-training\n```\n\n2. 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.\n\n3. 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.\n\n## Usage\n\nIn order to run the demo please download the required models available bellow and the associated data.\n\n```bash\nth main.lua -data path_to_300W_LP_dataset\n```\n\nIn order to see all the available options please run:\n\n```bash\nth main.lua --help\n```\n\n## Citation\n\n```\n@inproceedings{bulat2017far,\n  title={How far are we from solving the 2D \\\u0026 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)},\n  author={Bulat, Adrian and Tzimiropoulos, Georgios},\n  booktitle={International Conference on Computer Vision},\n  year={2017}\n}\n```\n\n## Acknowledgements\n\nThis pipeline is build around the ImageNet training code avaialable at \u003chttps://github.com/facebook/fb.resnet.torch\u003e and HourGlass(HG) code available at https://github.com/anewell/pose-hg-train\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F1adrianb%2Fface-alignment-training","html_url":"https://awesome.ecosyste.ms/projects/github.com%2F1adrianb%2Fface-alignment-training","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2F1adrianb%2Fface-alignment-training/lists"}