DeepMIM

Introduction

This repository is the official implementation of our

DeepMIM: Deep Supervision for Masked Image Modeling

Sucheng Ren, Fangyun Wei, Samuel Albanie, Zheng Zhang, Han Hu

Deep supervision, which involves extra supervisions to the intermediate features of a neural network, was widely used in image classification in the early deep learning era since it significantly reduces the training difficulty and eases the optimization like avoiding gradient vanish over the vanilla training. Nevertheless, with the emergence of normalization techniques and residual connection, deep supervision in image classification was gradually phased out. In this paper, we revisit deep supervision for masked image modeling (MIM) that pre-trains a Vision Transformer (ViT) via a mask-and-predict scheme. Experimentally, we find that deep supervision drives the shallower layers to learn more meaningful representations, accelerates model convergence, and expands attention diversities. Our approach, called DeepMIM, significantly boosts the representation capability of each layer. In addition, DeepMIM is compatible with many MIM models across a range of reconstruction targets.

News

Code and checkpoints are released!

Installation

We build the repo based on MAE

Pretraining

We pretrain DeepMIM on 32 V100 GPU with overall batch size of 4096 which is identical to that in MAE.

python -m torch.distributed.launch \
--nnodes 4 --node_rank $noderank \
--nproc_per_node 8 --master_addr $ip --master_port $port \
main_pretrain.py \
    --batch_size 128 \
    --model mae_vit_base_patch16 \
    --norm_pix_loss --clip_path /path/to/clip \
    --mask_ratio 0.75 \
    --epochs 1600 \
    --warmup_epochs 40 \
    --blr 1.5e-4 --weight_decay 0.05 \
    --data_path /path/to/imagenet/

Fine-tuning on ImageNet-1K (Classification)

Expected results: 85.6% Top-1 Accuracy log

python -m torch.distributed.launch --nproc_per_node=8 main_finetune.py \
    --batch_size 128 \
    --model vit_base_patch16 \
    --finetune ./output_dir/checkpoint-1599.pth \
    --epochs 100 \
    --output_dir ./out_finetune/ \
    --blr 1e-4 --layer_decay 0.6 \
    --weight_decay 0.05 --drop_path 0.1 --reprob 0.25 --mixup 0.8 --cutmix 1.0 \
    --dist_eval --data_path /path/to/imagenet

Fune-tuning on ADE20K (Semantic Segmentation)

Please refer Segmentation/README.md

Checkpoint

The pretrained and finetuned model on ImageNet-1K are available at

[Google Drive]

Comparison

Performance comparison on ImageNet-1K classification and ADE20K Semantic Segmentation.

Method	Model Size	Top-1	mIoU
MAE	ViT-B	83.6	48.1
DeepMIM-CLIP	ViT-B	85.6	53.1

Citation

If you have any question, feel free to contact Sucheng Ren :)

@article{ren2023deepmim,
    title={DeepMIM: Deep Supervision for Masked Image Modeling},
    author={Sucheng Ren and Fangyun Wei and Samuel Albanie and  Zheng Zhang and Han Hu},
    year={2023},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

OliverRensu/DeepMIM