Update README.md

README.md

@@ -24,13 +24,15 @@ Disclaimer: The team releasing Swin Transformer v2 did not write a model card fo
 
 The Swin Transformer is a type of Vision Transformer. It builds hierarchical feature maps by merging image patches (shown in gray) in deeper layers and has linear computation complexity to input image size due to computation of self-attention only within each local window (shown in red). It can thus serve as a general-purpose backbone for both image classification and dense recognition tasks. In contrast, previous vision Transformers produce feature maps of a single low resolution and have quadratic computation complexity to input image size due to computation of self-attention globally.
 
+Swin Transformer v2 adds 3 main improvements: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) a log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) a self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.
+
 ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/swin_transformer_architecture.png)
 
 [Source](https://paperswithcode.com/method/swin-transformer)
 
 ## Intended uses & limitations
 
-You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swin) to look for
+You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=swinv2) to look for
 fine-tuned versions on a task that interests you.
 
 ### How to use
@@ -56,7 +58,7 @@ predicted_class_idx = logits.argmax(-1).item()
 print("Predicted class:", model.config.id2label[predicted_class_idx])
 ```
 
-For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swin.html#).
+For more code examples, we refer to the [documentation](https://huggingface.co/transformers/model_doc/swinv2.html#).
 
 ### BibTeX entry and citation info