Heedless Backbones

GG 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

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Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

pretrain dataset

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ImageNet-1k

ImageNet-22k

JFT-300M

JFT-3B

MegData73M

pretrain method

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Supervised

Sup. + TL

FCMAE

MAE

CL

MAP

Legend

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Family

Pretrain Dataset

Classification Resolution

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Family

Pretrain Method

Classification Resolution

model	params (m)	pretrain	finetune	GFLOPs	Top-1
GG-T	28.0	IN-1k : Sup. : 300	— : — : —	4.5	82.0
GG-S	50.0	IN-1k : Sup. : 300	— : — : —	8.7	83.4

model	params (m)	pretrain	finetune	gflops	IN-1k
GG-T	28.0	IN-1k : Sup. : 300	— : — : —	4.5	82.0/—
GG-S	50.0	IN-1k : Sup. : 300	— : — : —	8.7	83.4/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
GG-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	265.0	44.1	66.7	48.3	—	—	—
GG-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 12	None	48.4	67.4	52.3	—	—	—
GG-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	355.0	45.7	68.3	49.9	—	—	—
GG-S	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 12	None	49.9	69.0	54.0	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
GG-T	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	265.0	39.9	63.3	42.4	—	—	—
GG-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 12	None	41.9	64.5	45.0	—	—	—
GG-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	355.0	41.3	65.3	44.0	—	—	—
GG-S	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 12	None	43.1	66.2	46.4	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
GG-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	942.0	47.2	—	—	46.4	—	—
GG-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1035.0	49.6	—	—	48.4	—	—