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MAE

Masked Autoencoders Are Scalable Vision Learners

Introduction

Official Repo

Code Snippet

Abstract

This paper shows that masked autoencoders (MAE) are scalable self-supervised learners for computer vision. Our MAE approach is simple: we mask random patches of the input image and reconstruct the missing pixels. It is based on two core designs. First, we develop an asymmetric encoder-decoder architecture, with an encoder that operates only on the visible subset of patches (without mask tokens), along with a lightweight decoder that reconstructs the original image from the latent representation and mask tokens. Second, we find that masking a high proportion of the input image, e.g., 75%, yields a nontrivial and meaningful self-supervisory task. Coupling these two designs enables us to train large models efficiently and effectively: we accelerate training (by 3x or more) and improve accuracy. Our scalable approach allows for learning high-capacity models that generalize well: e.g., a vanilla ViT-Huge model achieves the best accuracy (87.8%) among methods that use only ImageNet-1K data. Transfer performance in downstream tasks outperforms supervised pre-training and shows promising scaling behavior.

Usage

To use other repositories' pre-trained models, it is necessary to convert keys.

We provide a script beit2mmseg.py in the tools directory to convert the key of MAE model from the official repo to MMSegmentation style.

python tools/model_converters/beit2mmseg.py ${PRETRAIN_PATH} ${STORE_PATH}

E.g.

python tools/model_converters/beit2mmseg.py https://dl.fbaipublicfiles.com/mae/pretrain/mae_pretrain_vit_base.pth pretrain/mae_pretrain_vit_base_mmcls.pth

This script convert model from PRETRAIN_PATH and store the converted model in STORE_PATH.

In our default setting, pretrained models could be defined below:

pretrained models original models
mae_pretrain_vit_base_mmcls.pth 'mae_pretrain_vit_base'

Verify the single-scale results of the model:

sh tools/dist_test.sh \
configs/mae/upernet_mae-base_fp16_8x2_512x512_160k_ade20k.py \
upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth $GPUS --eval mIoU

Since relative position embedding requires the input length and width to be equal, the sliding window is adopted for multi-scale inference. So we set min_size=512, that is, the shortest edge is 512. So the multi-scale inference of config is performed separately, instead of '--aug-test'. For multi-scale inference:

sh tools/dist_test.sh \
configs/mae/upernet_mae-base_fp16_512x512_160k_ade20k_ms.py \
upernet_mae-base_fp16_8x2_512x512_160k_ade20k_20220426_174752-f92a2975.pth $GPUS --eval mIoU

Results and models

ADE20K

Method Backbone Crop Size pretrain pretrain img size Batch Size Lr schd Mem (GB) Inf time (fps) Device mIoU mIoU(ms+flip) config download
UPerNet ViT-B 512x512 ImageNet-1K 224x224 16 160000 9.96 7.14 V100 48.13 48.70 config model | log

Citation

@article{he2021masked,
  title={Masked autoencoders are scalable vision learners},
  author={He, Kaiming and Chen, Xinlei and Xie, Saining and Li, Yanghao and Doll{\'a}r, Piotr and Girshick, Ross},
  journal={arXiv preprint arXiv:2111.06377},
  year={2021}
}