Update README.md

README.md

@@ -7,21 +7,21 @@ datasets:
 - ade-20k
 ---
 
-# Mask
+# MaskFormer
 
-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). 
+MaskFormer model trained on ADE20k semantic segmentation (tiny-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
@@ -35,10 +35,10 @@ Here is how to use this model:
 
 >>> 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")
+>>> feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-ade")
 >>> inputs = feature_extractor(images=image, return_tensors="pt")
 
->>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade")
+>>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-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)`
@@ -46,11 +46,7 @@ Here is how to use this model:
 >>> 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)
+>>> predicted_semantic_map = feature_extractor.post_process_semantic_segmentation(outputs)[0]
 ```
 
-
-
 For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).