Comments (1)
Hi, you can use both Euclidean distance or cosine metric to compute the similarity score. For details, you can refer to the evaluation part in our codes. The performance of our released model has been reported in the table, please have a check. If your performance is not ideal enough, you can try to fine-tune the model on your own data or try some metric learning methods as a post-processing. Hope this helps. Thx.
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Related Issues (20)
- 人脸识别的PyTorch版本的使用案例可以有吗
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from face.evolve.