Are you sure you want to delete this task? Once this task is deleted, it cannot be recovered.
huolongshe b7bd9aa440 | 2 months ago | |
---|---|---|
app | 2 months ago | |
demo_data | 2 months ago | |
docs | 2 months ago | |
.gitignore | 2 months ago | |
Dockerfile | 2 months ago | |
LICENSE | 2 months ago | |
README.md | 2 months ago | |
application.yml | 2 months ago | |
build-docker.sh | 2 months ago | |
pack_model.py | 2 months ago | |
pip-install-reqs.sh | 2 months ago | |
requirements.txt | 2 months ago | |
run_model_server.py | 2 months ago |
Mask2Former model trained on Cityscapes instance segmentation (tiny-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository.
Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
模型来源: https://hf-mirror.com/facebook/mask2former-swin-tiny-cityscapes-instance
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》
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》