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
| license: mit | |
| library_name: timm | |
| # ViT_Fast — Model Weights | |
| This repository contains model checkpoints for the **Miss Patch** project. | |
| **Code and documentation**: [github.com/DoraemonXia/ViT_Fast](https://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 | | |