Create README.md

README.md

@@ -0,0 +1,68 @@
+---
+license: other
+tags:
+- vision
+- image-segmentation
+datasets:
+- coco
+widget:
+- src: http://images.cocodataset.org/val2017/000000039769.jpg
+  example_title: Cats
+- src: http://images.cocodataset.org/val2017/000000039770.jpg
+  example_title: Castle
+---
+
+# Mask2Former
+
+Mask2Former model trained on ADE20k semantic segmentation (small-sized version, Swin backbone). It was introduced in the paper [Masked-attention Mask Transformer for Universal Image Segmentation
+](https://arxiv.org/abs/2112.01527) and first released in [this repository](https://github.com/facebookresearch/Mask2Former/). 
+
+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.
+
+## Model description
+
+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, 
+[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
+without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
+
+![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png)
+
+## Intended uses & limitations
+
+You can use this particular checkpoint for panoptic segmentation. See the [model hub](https://huggingface.co/models?search=mask2former) to look for other
+fine-tuned versions on a task that interests you.
+
+### How to use
+
+Here is how to use this model:
+
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
+
+
+# load Mask2Former fine-tuned on ADE20k semantic segmentation
+processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-small-ade-semantic")
+model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-small-ade-semantic")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = processor(images=image, return_tensors="pt")
+
+with torch.no_grad():
+    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 processor for postprocessing
+result = processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+# we refer to the demo notebooks for visualization (see "Resources" section in the Mask2Former docs)
+predicted_semantic_map = result["segmentation"]
+```
+
+For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/mask2former).