Heedless Backbones

VBB 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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Cityscapes (val)

Cityscapes (test)

ADE20K (val)

ADE20K (test)

PASCAL VOC 2007 (val)

PASCAL VOC 2007 (test)

metric

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mIoU^ms

pAcc^ms

mAcc^ms

mIoU^ss

pAcc^ss

mAcc^ss

GFLOPs

head

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UPerNet

Mask2Former

Panoptic FPN

SETR

resolution

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512x2048

640x2560

axis

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Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

Legend

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Family

Pretrain Dataset

Semantic Segmentation Head

Semantic Segmentation Resolution

Semantic Segmentation Training Epochs

Legend attribute (second)

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Family

Pretrain Method

Semantic Segmentation Head

Semantic Segmentation Resolution

Semantic Segmentation Training Epochs

model	params (m)	pretrain	head	train	GFLOPs	mIoU^ms
VBB-T	29.0	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	996.0	51.8
VBB-B	85.0	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1328.0	52.9

model	params (m)	pretrain	finetune	gflops	IN-1k
VBB-T	29.0	IN-1k : Sup. : 300	— : — : —	5.1	83.8/—
VBB-B	85.0	IN-1k : Sup. : 300	— : — : —	15.8	84.9/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
VBB-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	286.0	47.7	69.3	52.1	—	—	—
VBB-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	530.0	49.3	70.9	54.5	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
VBB-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	286.0	42.6	66.3	46.3	—	—	—
VBB-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	530.0	44.2	68.4	47.9	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
VBB-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	996.0	51.8	—	—	50.3	—	—
VBB-B	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1328.0	52.9	—	—	52.5	—	—