Instructions to use ERISLab/FGIR-Backbones with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use ERISLab/FGIR-Backbones with timm:
import timm model = timm.create_model("hf_hub:ERISLab/FGIR-Backbones", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Add files using upload-large-folder tool
Browse files
cal_448/aircraft_tv_resnet101_cal.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:8797a8765ac375ccd56c92d4d5b27d07703a8c4b04421fd9442596a09845d03b
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size 197136427
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cal_448/aircraft_tv_resnet50_cal.pth
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oid sha256:92ab232b30aa8c83e8e5bad5a188f9f8b2f64117d699a5fba4930fcc647a58f9
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size 120845272
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cal_448/cars_tv_resnet50_cal.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:688c1651b22d2eb66b42cb2aff88e096edfa63cd217af412eea97c860ac3c80c
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size 146009788
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cal_448/cub_tv_resnet101_cal.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:4c118ea536ac1697941907a03b847883f45784471acbb41c83c14e85daacd79e
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size 223347598
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