---
license: apache-2.0
tags:
- image-classification
- vision
widget:
- src: >-
    https://huggingface.co/pamixsun/swinv2_tiny_for_glaucoma_classification/blob/main/example.jpg
  example_title: fundus image
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This is a Swin Transformer model fine-tuned on the REFUGE challenge dataset. It is able to classify an retinal fundns image into glaucoma and non-glaucoma.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

You can use the raw model for glaucoma classification based on retinal fundus images.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
import cv2
import torch

from transformers import AutoImageProcessor, Swinv2ForImageClassification

image = cv2.imread('./example.jpg')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

processor = AutoImageProcessor.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification")
model = Swinv2ForImageClassification.from_pretrained("pamixsun/swinv2_tiny_for_glaucoma_classification")

inputs = processor(image, return_tensors="pt")

with torch.no_grad():
    logits = model(**inputs).logits

# model predicts one of the 1000 ImageNet classes
predicted_label = logits.argmax(-1).item()
print(model.config.id2label[predicted_label])

```

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Model Card Contact

- pamixsun@gmail.com