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README.md
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@@ -12,7 +12,6 @@ We present EdgeFace- a lightweight and efficient face recognition network inspir
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* **Training**: EdgeFace-Base was trained on [Webface260M](https://www.face-benchmark.org/) dataset (12M and 4M subsets)
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* **Parameters**: 18.23M
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* **Task**: Zero-shot Multi-speaker TTS
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* **Output structure**: Batch of face images
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## Evaluation of EdgeFace
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## Running
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Please check the project [GitHub repository](https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface/)
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If you find our work useful, please cite the following publication:
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```
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```
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* **Training**: EdgeFace-Base was trained on [Webface260M](https://www.face-benchmark.org/) dataset (12M and 4M subsets)
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* **Parameters**: 18.23M
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* **Output structure**: Batch of face images
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## Evaluation of EdgeFace
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## Running EdgeFace-Base
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Please check the project [GitHub repository](https://gitlab.idiap.ch/bob/bob.paper.tbiom2023_edgeface/)
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If you find our work useful, please cite the following publication:
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```
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@article{edgeface,
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title={Edgeface: Efficient face recognition model for edge devices},
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author={George, Anjith and Ecabert, Christophe and Shahreza, Hatef Otroshi and Kotwal, Ketan and Marcel, Sebastien},
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journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
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year={2024}
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}
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```
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