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Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
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## Intended uses & limitations
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You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.
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Disclaimer: The team releasing SegFormer did not write a model card for this model so this model card has been written by the Hugging Face team.
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## Model description
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SegFormer consists of a hierarchical Transformer encoder and a lightweight all-MLP decode head to achieve great results on semantic segmentation benchmarks such as ADE20K and Cityscapes. The hierarchical Transformer is first pre-trained on ImageNet-1k, after which a decode head is added and fine-tuned altogether on a downstream dataset.
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## Intended uses & limitations
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You can use the raw model for semantic segmentation. See the [model hub](https://huggingface.co/models?other=segformer) to look for fine-tuned versions on a task that interests you.
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