Update README.md

README.md

@@ -1,27 +1,32 @@
 ---
-license: apache-2.0
+license: other
 tags:
 - vision
 - image-segmentation
 datasets:
-- coco
+- scene_parse_150
+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
+# MaskFormer
 
-Mask model trained on coco. 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). 
+MaskFormer model trained on COCO panoptic 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). 
 
-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.
+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.
 
 ## Model description
 
-MaskFormer addresses semantic segmentation with a mask classification paradigm instead.
+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.
 
 ![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
+You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other
 fine-tuned versions on a task that interests you.
 
 ### How to use
@@ -29,28 +34,28 @@ fine-tuned versions on a task that interests you.
 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)
+from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
+from PIL import Image
+import requests
+
+# load MaskFormer fine-tuned on COCO panoptic segmentation
+feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-coco")
+model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-coco")
+
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+inputs = feature_extractor(images=image, return_tensors="pt")
+
+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
+result = feature_extractor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
+# we refer to the demo notebooks for visualization (see "Resources" section in the MaskFormer docs)
+predicted_panoptic_map = result["segmentation"]
 ```
 
-
-
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).