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--- |
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license: apache-2.0 |
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tags: |
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- image-segmentation |
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- vision |
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- fundus |
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- optic disc |
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- optic cup |
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widget: |
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- src: >- |
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https://huggingface.co/pamixsun/swinv2_tiny_for_glaucoma_classification/resolve/main/example.jpg |
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example_title: fundus image |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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/). |
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It is specialized in automated analysis of retinal fundus photographs for glaucoma detection. |
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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. |
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To obtain optimal predictions, it is recommended to provide this model with standardized retinal fundus photographs captured using typical fundus imaging protocols. |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [Xu Sun](https://pamixsun.github.io) |
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- **Shared by:** [Xu Sun](https://pamixsun.github.io) |
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- **Model type:** Image classification |
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- **License:** Apache-2.0 |
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## Uses |
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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. --> |
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his pretrained model enables semantic segmentation of key anatomical structures， namely, the optic disc and optic cup, in retinal fundus images. |
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It takes fundus images as input and outputs the segmentation results. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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The model has undergone specialized training and fine-tuning exclusively using retinal fundus images, |
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with the objective to perform semantic segmentation of anatomical structures including the optic disc and optic cup. |
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Therefore, in order to derive optimal segmentation performance, it is imperative to ensure that only fundus images are entered as inputs to this model. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import cv2 |
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import torch |
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from transformers import AutoImageProcessor, SegformerForSemanticSegmentation |
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image = cv2.imread('./example.jpg') |
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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processor = AutoImageProcessor.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation") |
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model = SegformerForSemanticSegmentation.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation") |
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inputs = processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = self.seg_model(**inputs) |
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logits = outputs.logits.cpu() |
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upsampled_logits = nn.functional.interpolate( |
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logits, |
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size=image.shape[:2], |
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mode="bilinear", |
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align_corners=False, |
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) |
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pred_disc_cup = upsampled_logits.argmax(dim=1)[0].numpy().astype(np.uint8) |
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``` |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Model Card Contact |
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- pamixsun@gmail.com |
