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ConvMAE

ConvMAE: Masked Convolution Meets Masked Autoencoders

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

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

This repo is the official implementation of ConvMAE: 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.

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

Main Results on ImageNet-1K

Models #Params(M) Supervision Encoder Ratio Pretrain Epochs FT acc@1(%) LIN acc@1(%) 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 soon

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 300 36 109 0.7 51.4 45.4 -
Swin-L IN21K w/ labels 300 36 218 1.1 52.4 46.2 -
MViTv2-B IN21K w/ labels 300 36 73 0.6 53.1 47.4 -
MViTv2-L IN21K w/ labels 300 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 soon

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 soon

Getting Started

Prerequisites

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

Training and inference

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.

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