MaskFormer MaskFormer model trained on ADE20k semantic segmentation (base sized version, Swin backbone). It was introduced in the paper Per Pixel Classification is Not All You Need for Semantic Segmentation and first released in this repository. Disclaimer: The team releasing MaskFormer did not write a model card for this model so this model card has been written by the Hugging Face team. Model description MaskFormer addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Intended uses & limitations You can use this particular checkpoint for semantic segmentation. See the model hub to look for other fine tuned versions on a task that interests you. How to use Here is how to use this model: For more code examples, we refer to the documentation.
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