Heedless Backbones

VMamba Family

Paper | GitHub

axis

Select an option

Results

Parameters (M)

Images / Second

Publication Date

task

Select an option

---------

Object Detection

Instance Segmentation

Classification

Semantic Segmentation

Panoptic Segmentation

dataset

Select an option

---------

COCO (val)

COCO (test)

metric

Select an option

----------

mAP

AP₅₀

AP₇₅

mAP_s

mAP_m

mAP_l

GFLOPs

head

Select an option

---------

Mask R-CNN

Cascade Mask R-CNN

RetinaNet

DINO

HTC++

HTC

axis

Select an option

Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

pretrain dataset

Select an option

---------

JFT-3B

ImageNet-1k

ImageNet-22k

JFT-300M

pretrain method

Select an option

----------

Supervised

FCMAE

MAE

CL

Legend

Legend attribute

Select an option

Family

Pretrain Dataset

Pretrain Method

Instance Head

Instance Training Epochs

Legend attribute (second)

Select an option

----------

Family

Pretrain Method

Instance Head

Instance Training Epochs

model	params (m)	pretrain	head	train	GFLOPs	mAP
VMamba-T	30.0	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	271.0	47.3
VMamba-T	30.0	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 36	271.0	48.8
VMamba-S	50.0	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	349.0	48.7
VMamba-S	50.0	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 36	349.0	49.9
VMamba-B	89.0	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	485.0	49.2

model	params (m)	pretrain	finetune	gflops	IN-1k
VMamba-T	30.0	IN-1k : Sup. : 300	— : — : —	4.9	82.6/—
VMamba-S	50.0	IN-1k : Sup. : 300	— : — : —	8.7	83.6/—
VMamba-B	89.0	IN-1k : Sup. : 300	— : — : —	15.4	83.9/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
VMamba-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	271.0	47.3	69.3	52.0	—	—	—
VMamba-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 36	271.0	48.8	70.4	53.5	—	—	—
VMamba-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	349.0	48.7	70.0	53.4	—	—	—
VMamba-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 36	349.0	49.9	70.9	54.7	—	—	—
VMamba-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	485.0	49.2	71.4	54.0	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
VMamba-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	271.0	42.7	66.4	45.9	—	—	—
VMamba-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 36	271.0	43.7	67.4	47.0	—	—	—
VMamba-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	349.0	43.7	67.3	47.0	—	—	—
VMamba-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 36	349.0	44.2	68.2	47.7	—	—	—
VMamba-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	485.0	44.1	68.3	47.7	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
VMamba-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	949.0	48.8	—	—	47.9	—	—
VMamba-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1028.0	51.2	—	—	50.6	—	—
VMamba-B	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1170.0	51.6	—	—	51.0	—	—