Upload README.md

README.md

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+---
+license: apache-2.0
+tags:
+- vision
+- image-segmentatiom
+
+datasets:
+- ade-20k
+
+widget:
+- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg
+  example_title: House
+- src: https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000002.jpg
+  example_title: Castle
+
+---
+
+# Mask
+
+Mask model trained on ade-20k. 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). 
+
+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.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png)
+
+## Intended uses & limitations
+
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=maskformer) to look for
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model:
+
+```python
+>>> 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-base-ade")
+>>> inputs = feature_extractor(images=image, return_tensors="pt")
+
+>>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-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](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).