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<div align="center"> <h1>🚀Fast ConvMAE🚀</h1> <h3>Fast ConvMAE: Fast Pretraining of ConvMAE</h3> </div>

This repo is the faster implementation of ConvMAE: Masked Convolution Meets Masked Autoencoders

Updates

17/June/2022

Released the pre-training codes for ImageNet-1K.

Introduction

Fast ConvMAE framework is a superiorly fast masked modeling scheme via complementary masking and mixture of reconstrunctors based on the ConvMAE.

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Pretrain on ImageNet-1K

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

Fast ConvMAE-Base
50epoch pretrained checkpointsN/A
logsN/A

Main Results on COCO & ImageNet-1K

ModelsMaskingTokenizerBackbonePT EpochsPT HoursCOCO FT Epochs$AP^{Box}$$AP^{Mask}$ImageNet Finetune EpochsFinetune acc@1(%)ADE 20K mIoU
ConvMAE25 %RGBConvViT-B2005122550.845.410084.448.5
ConvMAE25 %RGBConvViT-B160040002553.247.110085.051.7
MAE25 %RGBViT-B1600206910050.344.910083.648.1
SimMIM100 %RGBSwin-B80016093650.444.410084.0-
GreenMIM25 %RGBSwin-B8008873650.044.110085.1-
ConvMAE100 %RGBConvViT-B502662551.045.410084.448.3
ConvMAE100 %C+TConvViT-B503332552.846.910085.052.7
ConvMAE100 %C+TConvViT-B1006662553.347.310085.252.8
ConvMAE100 %C+TConvViT-L200N/A25N/AN/A5086.754.5

Visualizations

NOTE: Grey patches are masked and colored ones are kept.

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Getting Started

Prerequisites

Training and evaluation

Acknowledgement

The pretraining and finetuning of our project are based on DeiT, MAE, and ConvMAE. Thanks for their wonderful work.

License

FastConvMAE is released under the MIT License.

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