Transformers
PyTorch
Safetensors
English
fundus
diabetic retinopathy
classification
Eval Results (legacy)
Instructions to use ClementP/FundusDRGrading-efficientnet_b0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ClementP/FundusDRGrading-efficientnet_b0 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ClementP/FundusDRGrading-efficientnet_b0", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c10d912ba436ce719b6bacba27ac31c16b4e280e8a2136608f2b5c2c9e7d30c
- Size of remote file:
- 16.3 MB
- SHA256:
- bf550b0dc634b967fe470d01b5d8fcf110eeebf58293306cbeb3577fa46deade
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.