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requirements.txt | 9 months ago | |
run_model_server.py | 9 months ago |
ArcFace为近几年人脸识别领域的代表性工作,被CVPR2019录取,该方法主要贡献是提出了ArcFace loss, 在$x_i$和$W_{ji}$之间的θ上加上角度间隔m(注意是加在了角θ上),以加法的方式惩罚深度特征与其相应权重之间的角度,从而同时增强了类内紧度和类间差异。由于提出的加性角度间隔(additive angular margin)惩罚与测地线距离间隔(geodesic distance margin)惩罚在归一化的超球面上相等,因此作者将该方法命名为ArcFace。此外作者在之后的几年内持续优化该算法,使其一直保持在sota性能。
本模型基于 ServiceBoot微服务引擎 进行服务化封装,参见: 《CubeAI模型开发指南》
$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py
一键式本地容器化部署和运行,参见: 《CubeAI模型独立部署指南》 或 CubeAI Docker Builder
本模型服务可一键发布至 CubeAI智立方平台 进行共享和部署,参见: 《CubeAI模型发布指南》
本模型提供了4个API接口:
API接口1:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "add_face"
"args": {
"name": <姓名>,
"img": <压缩图像的base64编码字符串(或其Data URL表示)>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": 1(添加人脸成功)|0(图像中无人脸或有多余一个人脸)|-1(已经存在相似人脸)
}
API接口2:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict"
"args": {
"img": <压缩图像的base64编码字符串(或其Data URL表示)>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": [<识别结果>, <gps信息>, <带姓名标注的base64编码图像URL>]
}
API接口3:
API端点: /api/stream/predict
HTTP方法: POST
HTTP请求体:
<二进制编码的压缩图像字节流>
HTTP响应体:
{
"status": "ok"|"err",
"value": [<识别结果>, <gps信息>, <带姓名标注的base64编码图像URL>]
}
API接口4:
API端点: /api/file/predict
HTTP方法: POST
HTTP请求体:
<用于HTTP文件上传的XHR格式请求体>
HTTP响应体:
{
"status": "ok"|"err",
"value": [<识别结果>, <gps信息>, <带姓名标注的base64编码图像URL>]
}
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Python Shell Dockerfile Text
Dear OpenI User
Thank you for your continuous support to the Openl Qizhi Community AI Collaboration Platform. In order to protect your usage rights and ensure network security, we updated the Openl Qizhi Community AI Collaboration Platform Usage Agreement in January 2024. The updated agreement specifies that users are prohibited from using intranet penetration tools. After you click "Agree and continue", you can continue to use our services. Thank you for your cooperation and understanding.
For more agreement content, please refer to the《Openl Qizhi Community AI Collaboration Platform Usage Agreement》