Instructions to use bitfount/RETFound_MAE_OCT_CNV_DME_DRU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use bitfount/RETFound_MAE_OCT_CNV_DME_DRU with timm:
import timm model = timm.create_model("hf_hub:bitfount/RETFound_MAE_OCT_CNV_DME_DRU", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Model card for RETFound_MAE_oct_dru_cnv_dme_nor
A model fine-tuned on the Kermany dataset of retinal OCT images, initialised from the base open-eye/RETFound_MAE model for OCT images.
This model achieves 85% accuracy in the task of classifying Retinal OCT images as either showing signs of Drusen, Choroidal Neovascularization (CNV), Diabetic Macular Oedema (DME), or alternatively showing none of these and being otherwise "Normal".
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