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Add SeaFormer model #21668
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Hi. I would like to work on this. |
Hi @inderpreetsingh01 thanks for opening the issue, SeaFormer definitely seems like a good addition to the library! Are you planning to work on this model? If not, @strankid could start working on it or you two could collaborate on a PR. In either case, you could take a look at our model addition guidelines, as well as the transformers code of other segmentation models such as SegFormer, MaskFormer, Mask2Former and OneFormer. |
hello, fancy to make SETR on board? |
@alaradirik should I begin with a WIP PR? |
Hi @alaradirik thanks for sharing the resources. I will be working on adding this model. @strankid if you want we can collaborate on this. @lzrobots i saw both SeaFormer and SETR use mmseg, we can look into it. |
@inderpreetsingh01 i'm down to collaborate! |
Great :) @strankid @inderpreetsingh01 you can ping me if you have questions about the library or need help with anything (e.g. model conversion). It'd be great if you could open a WIP PR, as it'd make it easier to ask / answer questions and do a preliminary review later down the road. |
thanks @alaradirik will do it. @strankid can you share your mail id so that we can connect on slack? |
@inderpreetsingh01 my email is apoorv96@gmail.com. Should I create the PR or would you like to? |
@strankid sure you can create the pr. |
@inderpreetsingh01 Saw you created the wip pr. Since you have my email, just contact me and let me know how you want to split the work. |
Model description
The computational cost and memory requirement render many computer vision models unsuitable on the mobile device, especially for the high-resolution per-pixel semantic segmentation task. SeaFormer (Squeeze-enhanced Axial Transformer) designed a generic attention block characterized by the formulation of squeeze Axial and detail enhancement. Coupled with a light segmentation head, they achieve the best trade-off between segmentation accuracy and latency on the ARM-based mobile devices on the ADE20K and Cityscapes datasets. They beat both the mobile-friendly rivals and Transformer-based counterparts with better performance and lower latency.
Open source status
Provide useful links for the implementation
Paper: https://arxiv.org/pdf/2301.13156.pdf
Code and weights: https://github.com/fudan-zvg/SeaFormer
Authors: @wwqq @lzrobots @speedinghzl
cc: @NielsRogge @alaradirik
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