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/MuReD/EfficientNetV2-L-MuReD.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/MuReD/EfficientNetV2-L-MuReD.pt
- Command line
-
hf download hf://TheodoreCousin/2-Net/EfficientNetV2-L/MuReD/EfficientNetV2-L-MuReD.pt
-
curl -L -o EfficientNetV2-L-MuReD.pt https://huggingface.co/TheodoreCousin/2-Net/resolve/main/EfficientNetV2-L/MuReD/EfficientNetV2-L-MuReD.pt
472 MB
- Xet hash:
- b0573a3785f8e1f7a9995cad0d1144175d474ac76bc3b630b2c81e3a803e3ce2
- Size of remote file:
- 472 MB
- SHA256:
- c2b9ee62004536263823aae588c4c12a28157a279bdd76d56137b49152ab2617
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