Image Classification
timm
PyTorch
medical-imaging
ophthalmology
fundus
retina
multi-label-classification
dinov2
lora
efficientnetv2
retfound
Instructions to use TheodoreCousin/2-Net with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use TheodoreCousin/2-Net with timm:
import timm model = timm.create_model("hf-hub:TheodoreCousin/2-Net", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Download EfficientNetV2-L/RFMiD/EfficientNetV2-L-RFMiD.pt from TheodoreCousin/2-Net: direct link, hf CLI and curl.
- Browser
- Download file 472 MB
-
https://huggingface.co/TheodoreCousin/2-Net/resolve/main/EfficientNetV2-L/RFMiD/EfficientNetV2-L-RFMiD.pt
- Command line
-
hf download hf://TheodoreCousin/2-Net/EfficientNetV2-L/RFMiD/EfficientNetV2-L-RFMiD.pt
-
curl -L -o EfficientNetV2-L-RFMiD.pt https://huggingface.co/TheodoreCousin/2-Net/resolve/main/EfficientNetV2-L/RFMiD/EfficientNetV2-L-RFMiD.pt
472 MB
- Xet hash:
- 9cdf583ccd31ee38ba71c8fb1ce9c20fceddacbfd09659c100e26e4fe7cfffcd
- Size of remote file:
- 472 MB
- SHA256:
- 76db98008c25637282d6ba0a6ea6901d976981aa9fc0738f48b080cd438fd459
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.