SegFormer (b1 sized) model fine tuned on ADE20k SegFormer model fine tuned on ADE20k at resolution 512x512. It was introduced in the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Xie et al. and first released in this repository. 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. Model description 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. Intended uses & limitations You can use the raw model for semantic segmentation. See the model hub to look for fine tuned versions on a task that interests you. How to use Here is how to use this model to classify an image of the COCO 2017 dataset into one of the 1,000 ImageNet classes: For more code examples, we refer to the documentation. BibTeX entry and citation info
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