Update README.md

README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 
-This model utilizes a Swin Transformer architecture and has undergone supervised fine-tuning on retinal fundus images from the [REFUGE challenge dataset](https://refuge.grand-challenge.org/). 
-It is specialized in automated analysis of retinal fundus photographs for glaucoma detection. 
-By extracting hierarchical visual features from input fundus images in a cross-scale manner, the model is able to effectively categorize each image as either glaucoma or non-glaucoma. Extensive experiments demonstrate that this model architecture achieves state-of-the-art performance on the REFUGE benchmark for fundus image-based glaucoma classification. 
-To obtain optimal predictions, it is recommended to provide this model with standardized retinal fundus photographs captured using typical fundus imaging protocols.
+This SegFormer model has undergone specialized fine-tuning on the [REFUGE challenge dataset](https://refuge.grand-challenge.org/), 
+a public benchmark for semantic segmentation of anatomical structures in retinal fundus images. 
+The fine-tuning enables expert-level segmentation of the optic disc and optic cup, two critical structures for ophthalmological diagnosis.
+
 
 ## Model Details
 
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 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-his pretrained model enables semantic segmentation of key anatomical structures， namely, the optic disc and optic cup, in retinal fundus images. 
+his pretrained model enables semantic segmentation of key anatomical structures, namely, the optic disc and optic cup, in retinal fundus images. 
 It takes fundus images as input and outputs the segmentation results.
 
 ## Bias, Risks, and Limitations