Heedless Backbones

SW 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

---------

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

Select an option

----------

Top-1

Top-5

GFLOPs

resolution

Select an option

----------

224x224

384x384

512x512

axis

Select an option

Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

pretrain dataset

Select an option

---------

ImageNet-1k

ImageNet-22k

JFT-300M

JFT-3B

MegData73M

pretrain method

Select an option

----------

Supervised

Sup. + TL

FCMAE

MAE

CL

MAP

Legend

Legend attribute

Select an option

Family

Pretrain Dataset

Classification Resolution

Legend attribute (second)

Select an option

----------

Family

Pretrain Method

Classification Resolution

model	params (m)	pretrain	finetune	GFLOPs	Top-1
SW-tiny	31.0	IN-1k : Sup. : 300	— : — : —	5.0	83.4
SW-small	56.0	IN-1k : Sup. : 300	— : — : —	9.4	83.9

model	params (m)	pretrain	finetune	gflops	IN-1k
SW-tiny	31.0	IN-1k : Sup. : 300	— : — : —	5.0	83.4/—
SW-small	56.0	IN-1k : Sup. : 300	— : — : —	9.4	83.9/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
SW-tiny	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	751.0	52.21	—	—	—	—	—
SW-small	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	839.0	52.7	—	—	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
SW-tiny	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	751.0	45.19	—	—	—	—	—
SW-small	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	839.0	45.67	—	—	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
SW-tiny	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	948.0	50.06	—	—	49.22	—	—
SW-small	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1039.0	50.83	—	—	49.79	—	—