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pip-install-reqs.sh | 10 months ago | |
requirements.txt | 9 months ago | |
run_model_server.py | 10 months ago |
RetinaFace为当前学术界和工业界精度较高的人脸检测和人脸关键点定位二合一的方法,被CVPR 2020 录取,该方法的主要贡献是: 引入关键点分支,可以在训练阶段引入关键点预测分支进行多任务学习,提供额外的互补特征,inference去掉关键点分支即可,并不会引入额外的计算量。
论文: https://arxiv.org/abs/1905.00641
代码: https://github.com/biubug6/Pytorch_Retinaface
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": "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": [<results>, <base64编码压缩图像URL>]
}
API接口2:
API端点: /api/data
HTTP方法: POST
HTTP请求体:
{
"action": "predict_video",
"args": {
"url": <云端视频流媒体URL, 例如: rtmp://localhost/live/ch1>
}
}
HTTP响应体:
{
"status": "ok"|"err",
"value": <(流媒体当前帧图像)AI处理结果的base64编码压缩图像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: 同API接口1
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: 同API接口1
No Description
Python Shell Dockerfile Text other
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》