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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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- scene_parse_150 |
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widget: |
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- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg |
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example_title: House |
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- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg |
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example_title: Castle |
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--- |
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# MaskFormer |
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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](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169). |
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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. |
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## Model description |
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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. |
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## Intended uses & limitations |
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You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other |
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fine-tuned versions on a task that interests you. |
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### How to use |
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Here is how to use this model: |
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```python |
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from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation |
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from PIL import Image |
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import requests |
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url = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade") |
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inputs = feature_extractor(images=image, return_tensors="pt") |
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model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade") |
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outputs = model(**inputs) |
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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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# you can pass them to feature_extractor for postprocessing |
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# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs) |
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predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0] |
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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/maskformer). |
