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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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The pretrained model provides glaucoma classification functionality solely based on analysis of retinal fundus images. |
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You may directly utilize the raw model without modification to categorize fundus images as either glaucoma or non-glaucoma. |
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This model is specialized in extracting discriminative features from fundus images to identify glaucoma manifestations. |
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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. |
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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 is specialized in analyzing retinal fundus images, and is trained exclusively on fundus image datasets to classify images as glaucoma or non-glaucoma. |
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Therefore, to obtain accurate predictions, it is crucial to only input fundus images when using this model. |
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Feeding other types of images may lead to meaningless results. |
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In summary, this model expects fundus images as input for glaucoma classification. |
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For the best performance, please adhere strictly to this input specification. |
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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, Swinv2ForImageClassification |
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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/swinv2_tiny_for_glaucoma_classification") |
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model = Swinv2ForImageClassification.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification") |
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inputs = processor(image, return_tensors="pt") |
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with torch.no_grad(): |
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logits = model(**inputs).logits |
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# model predicts either glaucoma or non-glaucoma. |
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predicted_label = logits.argmax(-1).item() |
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print(model.config.id2label[predicted_label]) |
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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 |
