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[DEST] add DEST model #2482
[DEST] add DEST model #2482
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@MeowZheng Please help to review the PR. Thanks! |
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The checkpoints and logs for smit-b0~5 are under https://drive.google.com/drive/folders/1MOnW41XKPRYseOtxh_wgsbgpt7xwmeLb?usp=sharing for reference |
Thanks for your contribution, please sign CLA first. |
Thanks for your reply, I have signed CLA. |
Hi @zhby99 Please modify this pr following the |
@MeowZheng Hi Miao, Thanks for your suggestion, I have moved all related codes into projects directory, looking forward to your comments! |
There might be 2 users in this pr boyinz and zhby99, and some commits are from boyinz who might be some default name of f your local git before you config it. CLA check is pending until you modify the commit history to remove boyinz. Moreover, there are some ci problems, but we have fixed it in #2520 Therefore, I suggest you might close this pr and create a new one based on the latest master. Sorry to make your modification many times. |
@MeowZheng Thanks for your comments, I have rebased this pr on latest master and reset the commits with single user id. |
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please follow the example project to add Prerequisites
and Checklist
.
This project needs to provide the checkpoints, so would you like to share the model checkpoints?
Please find the checkpoints and logs under https://drive.google.com/drive/folders/1MOnW41XKPRYseOtxh_wgsbgpt7xwmeLb?usp=sharing . |
As it may not be compatible between the different versions of mmseg and mmcv, we need to fix the version of them. There is an example https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x/projects/HieraSeg BTW, I think we may talk more efficiently on WeChat if it is convenient for you. Would you like to email me your wechat id? Thanks again for your contribution, and the documentation for community contribution processes needs to be improved |
Thanks for your suggestions. I have sent you an email with my wechat id. |
@MeowZheng Hi Miao, I have updated this PR with checklists and prerequisites |
zhby99,您好!您在MMSeg项目中给我们提的PR非常重要,感谢您付出私人时间帮助改进开源项目,相信很多开发者会从你的PR中受益。 |
Motivation
We are from NVIDIA and we have developed a simplified and inference-efficient transformer for dense prediction tasks. The method is based on SegFormer with hardware-friendly design choices, resulting in better accuracy and over 2x reduction in inference speed as compared to the baseline. We believe this model would be of particular interests to those who want to deploy an efficient vision transformer for production, and it is easily adaptable to other tasks. Therefore, we would like to contribute our method to mmsegmentation in order to benefit a larger audience.
The paper was accepted to Transformer for Vision workshop at CVPR 2022, here below are some resource links:
Paper https://arxiv.org/pdf/2204.13791.pdf (Table 3 shows the semseg results)
Code https://github.com/NVIDIA/DL4AGX/tree/master/DEST
A webinar on its application https://www.nvidia.com/en-us/on-demand/session/other2022-drivetraining/
Modification
Add backbone(smit.py) and head(dest_head.py) of DEST
BC-breaking (Optional)
N/A
Use cases (Optional)
N/A