Instructions to use amanyagami/Cifar100_Finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use amanyagami/Cifar100_Finetuned with timm:
import timm model = timm.create_model("hf-hub:amanyagami/Cifar100_Finetuned", pretrained=True) - Notebooks
- Google Colab
- Kaggle
Superseded — use
amanyagami/viyog-weightsinstead.This repo only contains 4 of the 20 CIFAR-100-finetuned checkpoints used in the Viyog paper (plus the CIFAR-10/GTSRB panels are missing entirely).
amanyagami/viyog-weightshas the complete, canonical set and is what the Viyog reproduction pipeline (experiments/01_download.pyin the viyog repo) fetches from.
- Downloads last month
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Model tree for amanyagami/Cifar100_Finetuned
Base model
timm/vit_base_patch16_224.augreg_in21k