Heedless Backbones

LocalVim 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
LocalVim-T	8.0	IN-1k : Sup. : 300	— : — : —	1.5	76.2
LocalVim-S	28.0	IN-1k : Sup. : 300	— : — : —	4.8	81.2

model	params (m)	pretrain	finetune	gflops	IN-1k
LocalVim-T	8.0	IN-1k : Sup. : 300	— : — : —	1.5	76.2/—
LocalVim-S	28.0	IN-1k : Sup. : 300	— : — : —	4.8	81.2/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
LocalVim-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 200	403.0	45.3	66.2	49.1	26.0	49.5	61.7

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
LocalVim-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 200	403.0	39.9	63.0	42.5	17.7	43.0	60.5

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
LocalVim-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	181.0	44.4	—	—	43.4	—	—
LocalVim-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	297.0	47.5	—	—	46.4	—	—