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[NeurIPS 2022] MCMAE: Masked Convolution Meets Masked Autoencoders

Peng Gao1, Teli Ma1, Hongsheng Li2, Ziyi Lin2, Jifeng Dai3, Yu Qiao1,

1 Shanghai AI Laboratory, 2 MMLab, CUHK, 3 Sensetime Research.

* We change the project name from ConvMAE to MCMAE.

This repo is the official implementation of MCMAE: Masked Convolution Meets Masked Autoencoders. It currently concludes codes and models for the following tasks:

ImageNet Pretrain: See PRETRAIN.md.
ImageNet Finetune: See FINETUNE.md.
Object Detection: See DETECTION.md.
Semantic Segmentation: See SEGMENTATION.md.
Video Classification: See VideoConvMAE.

Updates

14/Mar/2023

MR-MCMAE (a.k.a. ConvMAE-v2) paper released: Mimic before Reconstruct: Enhancing Masked Autoencoders with Feature Mimicking.

15/Sep/2022

Paper accepted at NeurIPS 2022.

9/Sep/2022

ConvMAE-v2 pretrained checkpoints are released.

21/Aug/2022

Official-ConvMAE-Det which follows official ViTDet codebase is released.

08/Jun/2022

🚀FastConvMAE🚀: significantly accelerates the pretraining hours (4000 single GPU hours => 200 single GPU hours). The code is going to be released at FastConvMAE.

27/May/2022

  1. The supported codes for ImageNet-1K pretraining.
  2. The supported codes and models for semantic segmentation are provided.

20/May/2022

Update results on video classification.

16/May/2022

The supported codes and models for COCO object detection and instance segmentation are available.

11/May/2022

  1. Pretrained models on ImageNet-1K for ConvMAE.
  2. The supported codes and models for ImageNet-1K finetuning and linear probing are provided.

08/May/2022

The preprint version is public at arxiv.

Introduction

ConvMAE framework demonstrates that multi-scale hybrid convolution-transformer can learn more discriminative representations via the mask auto-encoding scheme.

  • We present the strong and efficient self-supervised framework ConvMAE, which is easy to implement but show outstanding performances on downstream tasks.
  • ConvMAE naturally generates hierarchical representations and exhibit promising performances on object detection and segmentation.
  • ConvMAE-Base improves the ImageNet finetuning accuracy by 1.4% compared with MAE-Base. On object detection with Mask-RCNN, ConvMAE-Base achieves 53.2 box AP and 47.1 mask AP with a 25-epoch training schedule while MAE-Base attains 50.3 box AP and 44.9 mask AP with 100 training epochs. On ADE20K with UperNet, ConvMAE-Base surpasses MAE-Base by 3.6 mIoU (48.1 vs. 51.7).

tenser

Pretrain on ImageNet-1K

The following table provides pretrained checkpoints and logs used in the paper.

ConvMAE-Base
pretrained checkpoints download
logs download

The following results are for ConvMAE-v2 (pretrained for 200 epochs on ImageNet-1k).

model pretrained checkpoints ft. acc. on ImageNet-1k
ConvMAE-v2-Small download 83.6
ConvMAE-v2-Base download 85.7
ConvMAE-v2-Large download 86.8
ConvMAE-v2-Huge download 88.0

Main Results on ImageNet-1K

Models #Params(M) Supervision Encoder Ratio Pretrain Epochs FT acc@1(%) LIN acc@1(%) FT logs/weights LIN logs/weights
BEiT 88 DALLE 100% 300 83.0 37.6 - -
MAE 88 RGB 25% 1600 83.6 67.8 - -
SimMIM 88 RGB 100% 800 84.0 56.7 - -
MaskFeat 88 HOG 100% 300 83.6 N/A - -
data2vec 88 RGB 100% 800 84.2 N/A - -
ConvMAE-B 88 RGB 25% 1600 85.0 70.9 log/weight

Main Results on COCO

Mask R-CNN

Models Pretrain Pretrain Epochs Finetune Epochs #Params(M) FLOPs(T) box AP mask AP logs/weights
Swin-B IN21K w/ labels 90 36 109 0.7 51.4 45.4 -
Swin-L IN21K w/ labels 90 36 218 1.1 52.4 46.2 -
MViTv2-B IN21K w/ labels 90 36 73 0.6 53.1 47.4 -
MViTv2-L IN21K w/ labels 90 36 239 1.3 53.6 47.5 -
Benchmarking-ViT-B IN1K w/o labels 1600 100 118 0.9 50.4 44.9 -
Benchmarking-ViT-L IN1K w/o labels 1600 100 340 1.9 53.3 47.2 -
ViTDet IN1K w/o labels 1600 100 111 0.8 51.2 45.5 -
MIMDet-ViT-B IN1K w/o labels 1600 36 127 1.1 51.5 46.0 -
MIMDet-ViT-L IN1K w/o labels 1600 36 345 2.6 53.3 47.5 -
ConvMAE-B IN1K w/o lables 1600 25 104 0.9 53.2 47.1 log/weight

Main Results on ADE20K

UperNet

Models Pretrain Pretrain Epochs Finetune Iters #Params(M) FLOPs(T) mIoU logs/weights
DeiT-B IN1K w/ labels 300 16K 163 0.6 45.6 -
Swin-B IN1K w/ labels 300 16K 121 0.3 48.1 -
MoCo V3 IN1K 300 16K 163 0.6 47.3 -
DINO IN1K 400 16K 163 0.6 47.2 -
BEiT IN1K+DALLE 1600 16K 163 0.6 47.1 -
PeCo IN1K 300 16K 163 0.6 46.7 -
CAE IN1K+DALLE 800 16K 163 0.6 48.8 -
MAE IN1K 1600 16K 163 0.6 48.1 -
ConvMAE-B IN1K 1600 16K 153 0.6 51.7 log/weight

Main Results on Kinetics-400

Models Pretrain Epochs Finetune Epochs #Params(M) Top1 Top5 logs/weights
VideoMAE-B 200 100 87 77.8
VideoMAE-B 800 100 87 79.4
VideoMAE-B 1600 100 87 79.8
VideoMAE-B 1600 100 (w/ Repeated Aug) 87 80.7 94.7
SpatioTemporalLearner-B 800 150 (w/ Repeated Aug) 87 81.3 94.9
VideoConvMAE-B 200 100 86 80.1 94.3 Soon
VideoConvMAE-B 800 100 86 81.7 95.1 Soon
VideoConvMAE-B-MSD 800 100 86 82.7 95.5 Soon

Main Results on Something-Something V2

Models Pretrain Epochs Finetune Epochs #Params(M) Top1 Top5 logs/weights
VideoMAE-B 200 40 87 66.1
VideoMAE-B 800 40 87 69.3
VideoMAE-B 2400 40 87 70.3
VideoConvMAE-B 200 40 86 67.7 91.2 Soon
VideoConvMAE-B 800 40 86 69.9 92.4 Soon
VideoConvMAE-B-MSD 800 40 86 70.7 93.0 Soon

Getting Started

Prerequisites

  • Linux
  • Python 3.7+
  • CUDA 10.2+
  • GCC 5+

Training and evaluation

Visualization

tenser

Acknowledgement

The pretraining and finetuning of our project are based on DeiT and MAE. The object detection and semantic segmentation parts are based on MIMDet and MMSegmentation respectively. Thanks for their wonderful work.

License

ConvMAE is released under the MIT License.

Citation

@article{gao2022convmae,
  title={ConvMAE: Masked Convolution Meets Masked Autoencoders},
  author={Gao, Peng and Ma, Teli and Li, Hongsheng and Dai, Jifeng and Qiao, Yu},
  journal={arXiv preprint arXiv:2205.03892},
  year={2022}
}

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