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49 lines
1.9 KiB
Markdown
49 lines
1.9 KiB
Markdown
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## Subcenter ArcFace
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### 1. Motivation
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We introduce one extra hyperparameter (subcenter number `loss_K`) to ArcFace to relax the intra-class compactness constraint. In our experiments, we find ``loss_K=3`` can achieve a good balance between accuracy and robustness.
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### 2. Implementation
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The training process of Subcenter ArcFace is almost same as [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/ArcFace)
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The increased GPU memory consumption can be easily alleviated by our parallel framework.
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### 3. Training Dataset
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1. MS1MV0 (The noise rate is around 50%), download link ([baidu drive](https://pan.baidu.com/s/1bSamN5CLiSrxOuGi-Lx7tw), code ``8ql0``) ([dropbox](https://www.dropbox.com/sh/y2mj25uj440f7bl/AABc7pCJvUvxEcmXs8WYi9Zaa?dl=0))
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### 4. Training Steps
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1). Train Sub-center ArcFace (``loss_K=3``) on MS1MV0.
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2). Drop non-dominant subcenters and high-confident noisy data (``>75 degrees``).
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``
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python drop.py --data <ms1mv0-path> --model <step-1-pretrained-model> --threshold 75 --k 3 --output <ms1mv0-drop75-path>
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``
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3). Train ArcFace on the new ``MS1MV0-Drop75`` dataset.
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### 5. Pretrained Models and Logs
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[baidu drive](https://pan.baidu.com/s/1yikOW1Xzm1XIHu0uv0RdRw) code ``3jsh``. [gdrive](https://drive.google.com/file/d/1h8Ybz6mJ7n2IfLbDv2HUU37OdVHn7YPg/view?usp=sharing)
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### Citation
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If you find *Sub-center ArcFace* useful in your research, please consider to cite the following related papers:
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```
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@inproceedings{deng2020subcenter,
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title={Sub-center ArcFace: Boosting Face Recognition by Large-scale Noisy Web Faces},
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author={Deng, Jiankang and Guo, Jia and Liu, Tongliang and Gong, Mingming and Zafeiriou, Stefanos},
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booktitle={Proceedings of the IEEE Conference on European Conference on Computer Vision},
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year={2020}
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}
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```
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