README.md

Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling

Xin Ma¹, Chang Liu², Chunyu Xie³, Long Ye¹, Yafeng Deng³, Xiangyang Ji²,

¹ Communication University of China, ² Tsinghua University, ³ 360 AI Research.

This repo is the official implementation of Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling. It currently concludes codes and Pre-trained checkpoints.

Introduction

This work aims to alleviate the training inefficiency in masked image modeling (MIM). We believe the insufficient utilization of training signals should be responsible. To alleviate this issue, DMJD imposes a masking regulation to generate multiple complementary views facilitating more invisible tokens of each image to be reconstructed in the invisible reconstruction branch and further devise a dual-branch joint distillation architecture with an additional visible distillation branch to take full use of the input signals with superior targets. Extensive experiments and visualizations prove that with increased prediction rates, visible distillation, and superior targets can accelerate the training convergence yet not sacrificing the model generalization ability.

The contributions are summarized as follows:

• We propose a conceptually simple yet learning-efficient MIM training scheme, termed disjoint masking with joint distillation (DMJD), which targets increasing the utilization of per image at each training loop.
• We devise a multi-view generation strategy, i.e., disjoint masking (DM), to increase the prediction rate while keeping the corruption rate for efficient MIM and introduce the adaptive learning rate scale rule for better model generalization with augmented training batches.
• We develop a dual-branch architecture for joint distillation (JD), effectively pursuing representation learning on both visible and invisible regions with superior targets.
• We conduct sufficient evaluations justifying our DMJD can significantly accelerate model convergence and achieve outstanding performances on standard benchmark. Take an example, for linear probing classification on MaskFeat and ConvMAE baselines, DMJD achieves performance gains of 3.4% and 5.8% with 1.8× and 3× acceleration.

Getting Started

Setup

• Installation and preparation can follow the DeiT repo. Note that this repo is based on timm==0.4.12.

• Using DMJD with Docker.

Step 1. We provide a Dockerfile to build an image. Ensure that your docker version>=19.03.

# build an image with PyTorch 1.11, CUDA 11.3, and mmsegmentation
# If you prefer other versions, just modified the Dockerfile
docker build -t env:dmjd .

Step 2. Run it with

docker run --gpus all --shm-size=8g -itd -v {DATA_DIR}:/path/to/data -v {CODE_DIR}:/path/to/dmjd env:dmjd

Pre-training

The pre-training instruction is in PRETRAIN.md.

Evaluation

• The pre-trained checkpoints and its corresponding results on classification task.

	ConViT-Base	ConViT-Large
pre-trained checkpoint	download	download

• Main results on ImageNet-1K.

Method	Backbone	ETE	Gh.	Learning Target	FT acc@1(%)	LIN acc@1(%)
MaskFeat	ViT-B	1600	240	HOG	84.0	68.5
+DMJD	ViT-B	1600	132 (1.8×)	HOG	84.1 (+0.1)	71.9 (+3.4)
ConvMAE	ConViT-B	1600	300	RGB	85.0	70.9
+DMJD	ConViT-B	800	101 (3×)	HOG	85.2 (+0.2)	76.7 (+5.8)
ConvMAE	ConViT-L	800	480	RGB	86.2	-
+DMJD	ConViT-L	800	267 (1.8×)	HOG	86.3 (+0.1)	79.7

• The fine-tuning and linear probing instruction is in FINETUNE.md.

Acknowledgement

This repo is built on top of DeiT, MAE and ConvMAE. The semantic segmentation parts is based on MMSegmentation. Thanks for their wonderful work.

License

DMJD is released under the MIT License.

Citation

If you find this repository useful, please consider giving a star ⭐ and citation:

@Article{ma2022disjoint,
      title   = {Disjoint Masking with Joint Distillation for Efficient Masked Image Modeling}, 
      author  = {Xin Ma and Chang Liu and Chunyu Xie and Long Ye and Yafeng Deng and Xiangyang Ji},
      journal = {arXiv:2301.00230},
      year    = {2022},
}