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
Download pytorch_model.bin from seige-ml/DERETFound_AMD_AREDS: direct link, hf CLI and curl.
- Browser
- Download file 1.21 GB
-
https://huggingface.co/seige-ml/DERETFound_AMD_AREDS/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://seige-ml/DERETFound_AMD_AREDS/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/seige-ml/DERETFound_AMD_AREDS/resolve/main/pytorch_model.bin
1.21 GB
- Xet hash:
- 1eea004b33c9bc97bcf324644366d3fcc440b25f311fabb7e89a4e26b422844e
- Size of remote file:
- 1.21 GB
- SHA256:
- ab492fe595c4cbb6b3b6932d993bf5e9ea2f344133af80137c8c4d3d8b5c495c
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