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).

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

• See PRETRAIN.md for pretraining.
• See FINETUNE.md for pretrained model finetuning and linear probing.
• See DETECTION.md for using pretrained backbone on Mask RCNN.
• See SEGMENTATION.md for using pretrained backbone on UperNet.

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