Instructions to use seige-ml/DERETFound_AMD_AREDS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use seige-ml/DERETFound_AMD_AREDS with timm:
import timm model = timm.create_model("hf_hub:seige-ml/DERETFound_AMD_AREDS", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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Download README.md from seige-ml/DERETFound_AMD_AREDS: direct link, hf CLI and curl.
- Browser
- Download file 517 Bytes
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https://huggingface.co/seige-ml/DERETFound_AMD_AREDS/resolve/main/README.md
- Command line
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hf download hf://seige-ml/DERETFound_AMD_AREDS/README.md
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curl -L -o README.md https://huggingface.co/seige-ml/DERETFound_AMD_AREDS/resolve/main/README.md
517 Bytes
metadata
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
Model card for DERETFound_AMD_AREDS
DERETFound - Expertise-informed Generative Model Enables Ultra-High Data Efficiency for Building Generalist Medical Foundation Model
A model fine-tuned on the AREDS dataset DERETFound model for OCT images.
See the paper here: https://www.researchgate.net/publication/377358144_Expertise-informed_Generative_AI_Enables_Ultra-High_Data_Efficiency_for_Building_Generalist_Medical_Foundation_Model