Instructions to use 1999xia/ViT_Fast with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- timm
How to use 1999xia/ViT_Fast with timm:
import timm model = timm.create_model("hf_hub:1999xia/ViT_Fast", pretrained=True) - Notebooks
- Google Colab
- Kaggle
ViT_Fast โ Model Weights
This repository contains model checkpoints for the Miss Patch project.
Code and documentation: github.com/DoraemonXia/ViT_Fast
Available Weights
Baselines (ViT-B/16, IN-21K pretrained)
| File | Dataset | Test Acc |
|---|---|---|
checkpoints/cifar100_vit_b16_ft/best_model.pth |
CIFAR-100 | 91.69% |
checkpoints/oxford_pets_vit_b16_ft/best_model.pth |
Oxford Pets | 93.32% |
checkpoints/food101_vit_b16_in21k/best_model.pth |
Food-101 | 91.37% |
Downsampled (168x168)
| File | Dataset | Test Acc |
|---|---|---|
checkpoints/cifar100_vit_b16_img168/best_model.pth |
CIFAR-100 | 91.56% |
checkpoints/oxford_pets_vit_b16_img168/best_model.pth |
Oxford Pets | 90.65% |
MAE + Router
| File | Dataset | Keep Ratio | Test Acc |
|---|---|---|---|
checkpoints/cifar100_mae_patchsel_b16_keep75_distill/best_model.pth |
CIFAR-100 | 75% | 91.10% |
checkpoints/cifar100_mae_patchsel_b16_keep50/best_model.pth |
CIFAR-100 | 50% | 89.07% |
Distilled Routers
| File | Dataset |
|---|---|
checkpoints/router_distill_cifar100/router.pth |
CIFAR-100 |
checkpoints/router_distill_food101/router.pth |
Food-101 |
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