Heedless Backbones

MambaOut Family

Paper | GitHub

axis

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Results

Parameters (M)

Images / Second

Publication Date

task

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Object Detection

Instance Segmentation

Classification

Semantic Segmentation

Panoptic Segmentation

dataset

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ImageNet-1k

ImageNet-A

ImageNet-R

ImageNet-Sketch

ImageNet-C

ImageNet-C-bar

ImageNet-V2

ImageNet-ReaL

PASCAL VOC 2007 (val)

PASCAL VOC 2007 (test)

metric

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Top-1

Top-5

GFLOPs

resolution

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224x224

384x384

512x512

axis

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Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

pretrain dataset

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MegData73M

JFT-3B

JFT-300M

ImageNet-1k

ImageNet-22k

pretrain method

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Supervised

Sup. + TL

FCMAE

MAE

CL

Legend

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Family

Pretrain Dataset

Classification Resolution

Legend attribute (second)

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Family

Pretrain Method

Classification Resolution

model	params (m)	pretrain	finetune	GFLOPs	Top-1
MambaOut-Femto	7.3	IN-1k : Sup. : 300	— : — : —	1.2	78.9
MambaOut-T	26.5	IN-1k : Sup. : 300	— : — : —	4.5	82.7
MambaOut-S	48.5	IN-1k : Sup. : 300	— : — : —	9.0	84.1
MambaOut-B	84.8	IN-1k : Sup. : 300	— : — : —	15.8	84.2

model	params (m)	pretrain	finetune	gflops	IN-1k
MambaOut-Femto	7.3	IN-1k : Sup. : 300	— : — : —	1.2	78.9/—
MambaOut-T	26.5	IN-1k : Sup. : 300	— : — : —	4.5	82.7/—
MambaOut-S	48.5	IN-1k : Sup. : 300	— : — : —	9.0	84.1/—
MambaOut-B	84.8	IN-1k : Sup. : 300	— : — : —	15.8	84.2/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
MambaOut-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	262.0	45.1	67.3	49.6	—	—	—
MambaOut-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	354.0	47.4	69.1	52.4	—	—	—
MambaOut-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	495.0	47.4	69.3	52.2	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
MambaOut-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	262.0	41.0	64.1	44.1	—	—	—
MambaOut-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	354.0	42.7	66.1	46.2	—	—	—
MambaOut-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	495.0	43.0	66.4	46.3	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
MambaOut-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	938.0	48.6	—	—	47.4	—	—
MambaOut-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1032.0	50.6	—	—	49.5	—	—
MambaOut-B	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1178.0	51.0	—	—	49.6	—	—