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mmsegmentation/LICENSES.md
Boyin Zhang 409caf8548
[DEST] add DEST model (#2482) 
## 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](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsites.google.com%2Fview%2Ft4v-cvpr22%2Fpapers%3Fauthuser%3D0&data=05%7C01%7Cboyinz%40nvidia.com%7Cbf078d69821449d1f4c908dab5e8c7da%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638022308636438546%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=XtSgPQrbVgHxt5L9XkXF%2BGWvc95haB3kKPcHnsVIF3M%3D&reserved=0)
at CVPR 2022, here below are some resource links:
Paper
[https://arxiv.org/pdf/2204.13791.pdf](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Farxiv.org%2Fpdf%2F2204.13791.pdf&data=05%7C01%7Cboyinz%40nvidia.com%7Cbf078d69821449d1f4c908dab5e8c7da%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638022308636438546%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=X%2FCVoa6PFA09EHfClES36QOa5NvbZu%2F6IDfBVwiYywU%3D&reserved=0)
(Table 3 shows the semseg results)
Code
[https://github.com/NVIDIA/DL4AGX/tree/master/DEST](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2FNVIDIA%2FDL4AGX%2Ftree%2Fmaster%2FDEST&data=05%7C01%7Cboyinz%40nvidia.com%7Cbf078d69821449d1f4c908dab5e8c7da%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638022308636438546%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=9DLQZpEq1cN75%2FDf%2FniUOOUFS1ABX8FEUH02O6isGVQ%3D&reserved=0)
A webinar on its application
[https://www.nvidia.com/en-us/on-demand/session/other2022-drivetraining/](https://nam11.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nvidia.com%2Fen-us%2Fon-demand%2Fsession%2Fother2022-drivetraining%2F&data=05%7C01%7Cboyinz%40nvidia.com%7Cbf078d69821449d1f4c908dab5e8c7da%7C43083d15727340c1b7db39efd9ccc17a%7C0%7C0%7C638022308636438546%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=8jrBC%2Bp3jGxiaW4vtSfhh6GozC3tRqGNjNoALM%2FOYxs%3D&reserved=0)

## Modification

Add backbone(smit.py) and head(dest_head.py) of DEST

## BC-breaking (Optional)

N/A

## Use cases (Optional)

N/A

---------

Co-authored-by: MeowZheng <meowzheng@outlook.com>
2023-02-16 17:42:34 +08:00

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Licenses for special features

In this file, we list the features with other licenses instead of Apache 2.0. Users should be careful about adopting these features in any commercial matters.

Feature Files License
SegFormer mmseg/models/decode_heads/segformer_head.py NVIDIA License
DEST mmseg/models/backbones/smit.py mmseg/models/decode_heads/dest_head.py NVIDIA License