nielsr HF staff commited on
Commit
51545d6
1 Parent(s): 46a205a

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +4 -6
README.md CHANGED
@@ -9,19 +9,19 @@ datasets:
9
 
10
  # MaskFormer
11
 
12
- MaskFormer 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).
13
 
14
- 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.
15
 
16
  ## Model description
17
 
18
- MaskFormer addresses semantic segmentation with a mask classification paradigm instead.
19
 
20
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png)
21
 
22
  ## Intended uses & limitations
23
 
24
- You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=maskformer) to look for
25
  fine-tuned versions on a task that interests you.
26
 
27
  ### How to use
@@ -46,9 +46,7 @@ Here is how to use this model:
46
  >>> masks_queries_logits = outputs.masks_queries_logits
47
 
48
  >>> # you can pass them to feature_extractor for postprocessing
49
- >>> output = feature_extractor.post_process_segmentation(outputs)
50
  >>> output = feature_extractor.post_process_semantic_segmentation(outputs)
51
- >>> output = feature_extractor.post_process_panoptic_segmentation(outputs)
52
  ```
53
 
54
  For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).
 
9
 
10
  # MaskFormer
11
 
12
+ MaskFormer model trained on ADE20k semantic segmentation. 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).
13
 
14
+ 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.
15
 
16
  ## Model description
17
 
18
+ 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.
19
 
20
  ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png)
21
 
22
  ## Intended uses & limitations
23
 
24
+ You can use this particular checkpoint for semantic segmentation. See the [model hub](https://huggingface.co/models?search=maskformer) to look for other
25
  fine-tuned versions on a task that interests you.
26
 
27
  ### How to use
 
46
  >>> masks_queries_logits = outputs.masks_queries_logits
47
 
48
  >>> # you can pass them to feature_extractor for postprocessing
 
49
  >>> output = feature_extractor.post_process_semantic_segmentation(outputs)
 
50
  ```
51
 
52
  For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).