Heedless Backbones

DeBiFormer Family

Paper | GitHub

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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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COCO (val)

COCO (test)

Cityscapes (val)

Cityscapes (test)

ADE20K (val)

ADE20K (test)

metric

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mAP

AP₅₀

AP₇₅

mAP_s

mAP_m

mAP_l

GFLOPs

head

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Mask R-CNN

Cascade Mask R-CNN

Mask2Former

HTC++

HTC

Panoptic FPN

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Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

Legend

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Family

Pretrain Dataset

Instance Head

Instance Training Epochs

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Family

Pretrain Method

Instance Head

Instance Training Epochs

No Results

model	params (m)	pretrain	finetune	gflops	IN-1k
DeBiFormer-T	21.4	IN-1k : Sup. : 300	— : — : —	2.6	81.9/—
DeBiFormer-S	44.0	IN-1k : Sup. : 300	— : — : —	5.4	83.9/—
DeBiFormer-B	77.0	IN-1k : Sup. : 300	— : — : —	11.8	84.4/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
DeBiFormer-S	IN-1k : Sup. : 300	RetinaNet	COCO (train) : 12	None	45.6	66.6	48.9	28.7	49.3	61.6
DeBiFormer-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	None	47.5	69.7	52.1	—	—	—
DeBiFormer-B	IN-1k : Sup. : 300	RetinaNet	COCO (train) : 12	None	47.1	68.2	50.2	30.3	51.1	63.0
DeBiFormer-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	None	48.5	70.2	53.3	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
DeBiFormer-S	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	None	42.5	66.2	45.7	—	—	—
DeBiFormer-B	IN-1k : Sup. : 300	Mask R-CNN	COCO (train) : 12	None	43.2	67.2	46.4	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
DeBiFormer-S	IN-1k : Sup. : 300	Panoptic FPN	ADE20K (train) : 128 : 512	None	—	—	—	49.2	—	—
DeBiFormer-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	None	—	—	—	50.0	—	—
DeBiFormer-B	IN-1k : Sup. : 300	Panoptic FPN	ADE20K (train) : 128 : 512	None	—	—	—	50.6	—	—
DeBiFormer-B	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	None	52.0	—	—	51.4	—	—