metadata

license: apache-2.0
tags:
  - vision
  - image-segmentation
datasets:
  - ade-20k

MaskFormer

MaskFormer model trained on ade-20k. 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 Mask 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 semantic segmentation with a mask classification paradigm instead.

Intended uses & limitations

You can use the raw model for image classification. 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:

>>> from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
>>> from PIL import Image
>>> import requests

>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-large-ade")
>>> inputs = feature_extractor(images=image, return_tensors="pt")

>>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-large-ade")
>>> outputs = model(**inputs)
>>> # model predicts class_queries_logits of shape `(batch_size, num_queries)`
>>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)`
>>> class_queries_logits = outputs.class_queries_logits
>>> masks_queries_logits = outputs.masks_queries_logits

>>> # you can pass them to feature_extractor for postprocessing
>>> output = feature_extractor.post_process_segmentation(outputs)
>>> output = feature_extractor.post_process_semantic_segmentation(outputs)
>>> output = feature_extractor.post_process_panoptic_segmentation(outputs)

For more code examples, we refer to the documentation.