Model details
模型介绍 中文整理 TransFace模型介绍
人脸识别模型TransFace，论文被**ICCV23**录取。本文提出了一种基于ViT的人脸识别新框架。我们并没有为ViT引入任何较大的结构改进，而是从data-centric角度提出了两个学习策略：DPAP和EHSM，这确保了两个策略的通用性和灵活性。一系列在popular face benchmarks上的实验结果表明了我们TransFace模型的优越性。论文链接。
模型结构
模型使用方式和使用范围
本模型可以检测输入图片中人脸的特征。
代码范例
使用方式
推理：输入经过对齐的人脸图片(112x112)，返回人脸特征向量(512维)，为便于体验，集成了人脸检测和关键点模型RetinaFace，输入两张图片，各自进行人脸检测选择最大脸并对齐后提取特征，然后返回相似度比分
目标场景
人脸识别应用广泛，如考勤，通行，人身核验，智慧安防等场景
模型局限性及可能偏差
由于模型较大，目前仅支持GPU推理。
当前版本在python 3.7环境测试通过，其他环境下可用性待测试
模型性能指标
Method IJBC(1e-5) IJBC(1e-4) IJBC(1e-3)
----------- ------------ ------------ ------------ TransFace 96.18 97.45 98.17
来源说明
本模型及代码来自达摩院自研技术。
优先采用原作者提供的中文模型卡片；仅有英文说明时由闲社AI翻译整理。重要参数、许可证和商用范围请以原文为准。
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完整仓库可能包含多个权重格式，因此需要的磁盘空间可能大于单一格式的模型大小。下载文件不等于完成模型部署。
使用前须知 运行环境 运行模型需要的设备、工具与依赖，以原作者说明为准。
授权范围 是否允许商用、修改和分发，请查看模型许可证。
闲社提供资料与镜像下载方法，不代表已运行模型或完成安全审计。下载的代码应先检查，再执行。
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cv_vit_face-recognition model download Download the full repository or choose a version or file.
Choose a download source ModelScope · China direct Pinned ModelScope version: 2ae0cbef71accec768a0fa10d3ba938ebeba17c0. Version identifiers can differ across sites. The alternative source was provided after checking weights and runtime configuration; this is not proof of a successful model run.
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