Heedless Backbones

LiSA-Swin 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

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
LiSA-Swin-T	29.74	IN-1k : Sup. : 300	— : — : —	4.72	82.25

model	params (m)	pretrain	finetune	gflops	IN-1k
LiSA-Swin-T	29.74	IN-1k : Sup. : 300	— : — : —	4.72	82.25/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
LiSA-Swin-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	269.0	44.5	67.4	48.7	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
LiSA-Swin-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	269.0	40.3	63.9	43.1	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
LiSA-Swin-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	947.0	—	—	—	45.03	—	56.4