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This model was released on 2021-12-02 and added to Hugging Face Transformers on 2023-01-16.

Mask2Former

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Overview

The Mask2Former model was proposed in Masked-attention Mask Transformer for Universal Image Segmentation by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar. Mask2Former is a unified framework for panoptic, instance and semantic segmentation and features significant performance and efficiency improvements over MaskFormer.

The abstract from the paper is the following:

Image segmentation groups pixels with different semantics, e.g., category or instance membership. Each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).

<small> Mask2Former architecture. Taken from the <a href="https://huggingface.co/papers/2112.01527">original paper.</a> </small>

This model was contributed by Shivalika Singh and Alara Dirik. The original code can be found here.

Usage tips

• Mask2Former uses the same preprocessing and postprocessing steps as MaskFormer. Use [Mask2FormerImageProcessor] or [AutoImageProcessor] to prepare images and optional targets for the model.
• To get the final segmentation, depending on the task, you can call [~Mask2FormerImageProcessor.post_process_semantic_segmentation] or [~Mask2FormerImageProcessor.post_process_instance_segmentation] or [~Mask2FormerImageProcessor.post_process_panoptic_segmentation]. All three tasks can be solved using [Mask2FormerForUniversalSegmentation] output, panoptic segmentation accepts an optional label_ids_to_fuse argument to fuse instances of the target object/s (e.g. sky) together.

Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mask2Former.

• Demo notebooks regarding inference + fine-tuning Mask2Former on custom data can be found here.
• Scripts for finetuning [Mask2Former] with [Trainer] or Accelerate can be found here.

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it. The resource should ideally demonstrate something new instead of duplicating an existing resource.

Mask2FormerConfig

[[autodoc]] Mask2FormerConfig

MaskFormer specific outputs

[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerModelOutput

[[autodoc]] models.mask2former.modeling_mask2former.Mask2FormerForUniversalSegmentationOutput

Mask2FormerModel

[[autodoc]] Mask2FormerModel - forward

Mask2FormerForUniversalSegmentation

[[autodoc]] Mask2FormerForUniversalSegmentation - forward

Mask2FormerImageProcessor

[[autodoc]] Mask2FormerImageProcessor - preprocess - post_process_semantic_segmentation - post_process_instance_segmentation - post_process_panoptic_segmentation

Mask2FormerImageProcessorPil

[[autodoc]] Mask2FormerImageProcessorPil - preprocess - post_process_semantic_segmentation - post_process_instance_segmentation - post_process_panoptic_segmentation