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README.md

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-This is a Swin Transformer model fine-tuned on the [REFUGE challenge dataset](https://refuge.grand-challenge.org/). It is able to classify an retinal fundns image into glaucoma and non-glaucoma.
+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.
 
 ## Model Details
 
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 - **Developed by:** [Xu Sun](https://pamixsun.github.io)
 - **Shared by:** [Xu Sun](https://pamixsun.github.io)
 - **Model type:** Image classification
 - **License:** Apache-2.0
 
-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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 ## Uses
 
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-You can use the raw model for glaucoma classification based on retinal fundus images.
+The pretrained model provides glaucoma classification functionality solely based on analysis of retinal fundus images. 
+You may directly utilize the raw model without modification to categorize fundus images as either glaucoma or non-glaucoma. 
+This model is specialized in extracting discriminative features from fundus images to identify glaucoma manifestations. 
+However, to achieve optimal performance, it is highly recommended to fine-tune the model on a representative fundus image dataset prior to deployment in real-world applications.
 
 ## Bias, Risks, and Limitations
 
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-The model is trained/fine-tuned on retinal fundus images only, and was intended to classify glaucoma and non-glaucoma images. 
-Thus please make sure to feed only fundus image into the model to obtain reasonable results.
+The model is specialized in analyzing retinal fundus images, and is trained exclusively on fundus image datasets to classify images as glaucoma or non-glaucoma. 
+Therefore, to obtain accurate predictions, it is crucial to only input fundus images when using this model. 
+Feeding other types of images may lead to meaningless results. 
+In summary, this model expects fundus images as input for glaucoma classification. 
+For the best performance, please adhere strictly to this input specification.
 
 ## How to Get Started with the Model