Instructions to use pamixsun/segformer_for_optic_disc_cup_segmentation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pamixsun/segformer_for_optic_disc_cup_segmentation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="pamixsun/segformer_for_optic_disc_cup_segmentation")# Load model directly from transformers import AutoImageProcessor, SegformerForSemanticSegmentation processor = AutoImageProcessor.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation") model = SegformerForSemanticSegmentation.from_pretrained("pamixsun/segformer_for_optic_disc_cup_segmentation") - Inference
- Notebooks
- Google Colab
- Kaggle
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README.md
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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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It takes fundus images as input and outputs the segmentation results.
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## Bias, Risks, and Limitations
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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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This 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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