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--- |
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title: Face Analysis (facetorch) |
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emoji: 🥸 |
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colorFrom: red |
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colorTo: black |
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sdk: docker |
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app_port: 7860 |
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pinned: false |
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license: apache-2.0 |
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task_categories: |
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- face-detection |
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- face-representation |
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- face-verification |
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- facial-expression-recognition |
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- deepfake-detection |
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- face-alignment |
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- 3D-face-alignment |
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duplicated_from: tomas-gajarsky/facetorch-app |
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--- |
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#  facetorch |
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[](https://pypi.org/project/facetorch/) |
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[](https://anaconda.org/conda-forge/facetorch) |
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[](https://raw.githubusercontent.com/tomas-gajarsky/facetorch/main/LICENSE) |
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<a href="https://github.com/psf/black"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a> |
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[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) |
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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: |
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1. configure using [Hydra](https://hydra.cc/docs/intro/) (OmegaConf) |
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2. reproduce with [conda-lock](https://github.com/conda-incubator/conda-lock) and [Docker](https://docs.docker.com/get-docker/) |
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3. accelerate on CPU and GPU with [TorchScript](https://pytorch.org/docs/stable/jit.html) |
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4. extend by uploading a model file to Google Drive and adding a config YAML file to the repository |
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Please, use the library responsibly with caution and follow the |
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[ethics guidelines for Trustworthy AI from European Commission](https://ec.europa.eu/futurium/en/ai-alliance-consultation.1.html). |
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The models are not perfect and may be biased. |
