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pack_model.py | 8 months ago | |
pip-install-reqs.sh | 8 months ago | |
requirements.txt | 8 months ago | |
run_model_server.py | 8 months ago |
BEiTv2模型先在ImageNet-1k(1419万张图像,1000个类别)数据集上以自监督的方式进行预训练,并在ImageNet-21k(1400万张图像,21,841个类别)数据集上进行微调,得到的预训练模型在下游ImageNet-1k分类任务上进行微调后,BEiTv2-base在ImageNet-1k验证集上达到了86.5%的top-1精度,在其它下游任务上也分别达到了SOTA的结果。
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》
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Python Shell Text Dockerfile
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》