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[MaskFormer](https://arxiv.org/abs/2107.06278) 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
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without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
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![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/
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## Intended uses & limitations
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[MaskFormer](https://arxiv.org/abs/2107.06278) 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
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without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
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![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png)
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## Intended uses & limitations
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