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+ ---
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+ license: other
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+ tags:
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+ - vision
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+ - image-segmentation
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+ datasets:
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+ - coco
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+ widget:
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+ - src: http://images.cocodataset.org/val2017/000000039769.jpg
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+ example_title: Cats
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+ - src: http://images.cocodataset.org/val2017/000000039770.jpg
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+ example_title: Castle
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+ ---
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+
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+ # Mask2Former
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+
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+ Mask2Former model trained on ADE20k semantic segmentation (base-IN21k version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
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+ ](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/).
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+
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+ Disclaimer: The team releasing Mask2Former 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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+
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+ ## Model description
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+
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+ Mask2Former 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. Mask2Former outperforms the previous SOTA,
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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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+
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+ ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png)
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+
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+ ## Intended uses & limitations
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+
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+ You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other
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+ fine-tuned versions on a task that interests you.
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+
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+ ### How to use
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+
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+ Here is how to use this model:
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+
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+ ```python
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+ import requests
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+ import torch
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+ from PIL import Image
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+ from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
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+
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+
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+ # load Mask2Former fine-tuned on COCO panoptic segmentation
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+ processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-IN21k-ade-semantic")
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+ model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-IN21k-ade-semantic")
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+
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+ url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+ inputs = processor(images=image, return_tensors="pt")
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+
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+ with torch.no_grad():
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+ outputs = model(**inputs)
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+
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+ # model predicts class_queries_logits of shape `(batch_size, num_queries)`
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+ # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
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+ class_queries_logits = outputs.class_queries_logits
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+ masks_queries_logits = outputs.masks_queries_logits
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+
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+ # you can pass them to processor for postprocessing
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+ result = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
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+ # we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
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+ predicted_semantic_map = result["segmentation"]
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+ ```
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+
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+ For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).