• 图像分割(mask2former-swin-base-coco-instance)
• • 模型应用开发和部署
  • • 模型服务化
    • 直接源代码运行
    • 本地容器化部署
    • 云原生网络部署
    • 更多CubeAI模型服务，参见： 《CubeAI服务原生模型示范库》

图像分割(mask2former-swin-base-coco-instance)

Mask2Former model trained on COCO instance segmentation (base-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-base-coco-instance

模型应用开发和部署

模型服务化

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》

直接源代码运行

$ sh pip-install-reqs.sh
$ serviceboot start
或
$ python3 run_model_server.py

本地容器化部署

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

云原生网络部署

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》

更多CubeAI模型服务，参见： 《CubeAI服务原生模型示范库》