metadata
datasets:
- imagenet-1k
tags:
- mae
- crossmae
pipeline_tag: image-classification
library_name: pytorch
license: cc-by-nc-4.0
CrossMAE: Rethinking Patch Dependence for Masked Autoencoders
by Letian Fu*, Long Lian*, Renhao Wang, Baifeng Shi, Xudong Wang, Adam Yala†, Trevor Darrell†, Alexei A. Efros†, Ken Goldberg† at UC Berkeley and UCSF
[Paper] | [Project Page] | [Citation]
This repo has the models for CrossMAE: Rethinking Patch Dependence for Masked Autoencoders.
Please take a look at the GitHub repo to see instructions on pretraining, fine-tuning, and evaluation with these models.
| ViT-Small | ViT-Base | ViT-Base448 | ViT-Large | ViT-Huge | |
|---|---|---|---|---|---|
| pretrained checkpoint | download | download | download | download | download |
| fine-tuned checkpoint | download | download | download | download | download |
| Reference ImageNet accuracy (ours) | 79.318 | 83.722 | 84.598 | 85.432 | 86.256 |
| MAE ImageNet accuracy (baseline) | 84.8 | 85.9 |
Citation
Please give us a star 🌟 on Github to support us!
Please cite our work if you find our work inspiring or use our code in your work:
@article{
fu2025rethinking,
title={Rethinking Patch Dependence for Masked Autoencoders},
author={Letian Fu and Long Lian and Renhao Wang and Baifeng Shi and XuDong Wang and Adam Yala and Trevor Darrell and Alexei A Efros and Ken Goldberg},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2025},
url={https://openreview.net/forum?id=JT2KMuo2BV},
note={}
}