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| license: apache-2.0 | |
| emoji: 👁 | |
| colorFrom: red | |
| colorTo: gray | |
| short_description: Facial expressions, 3D landmarks, embeddings, recognition. | |
| sdk: docker | |
| pinned: true | |
| #  facetorch | |
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| [](https://pypi.org/project/facetorch/) | |
| [](https://anaconda.org/conda-forge/facetorch) | |
| [](https://raw.githubusercontent.com/tomas-gajarsky/facetorch/main/LICENSE) | |
| <a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a> | |
| [Documentation](https://tomas-gajarsky.github.io/facetorch/facetorch/index.html), [Docker Hub](https://hub.docker.com/repository/docker/tomasgajarsky/facetorch) [(GPU)](https://hub.docker.com/repository/docker/tomasgajarsky/facetorch-gpu) | |
| Facetorch is a Python library that can detect faces and analyze facial features using deep neural networks. The goal is to gather open-sourced face analysis models from the community, optimize them for performance using TorchScript and combine them to create a face analysis tool that one can: | |
| 1. configure using [Hydra](https://hydra.cc/docs/intro/) (OmegaConf) | |
| 2. reproduce with [conda-lock](https://github.com/conda-incubator/conda-lock) and [Docker](https://docs.docker.com/get-docker/) | |
| 3. accelerate on CPU and GPU with [TorchScript](https://pytorch.org/docs/stable/jit.html) | |
| 4. extend by uploading a model file to Google Drive and adding a config YAML file to the repository | |
| Please, use the library responsibly with caution and follow the | |
| [ethics guidelines for Trustworthy AI from European Commission](https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html). | |
| The models are not perfect and may be biased. |