Heedless Backbones

SLaK 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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Cityscapes (val)

Cityscapes (test)

ADE20K (val)

ADE20K (test)

PASCAL VOC 2007 (val)

PASCAL VOC 2007 (test)

metric

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mIoU^ms

pAcc^ms

mAcc^ms

mIoU^ss

pAcc^ss

mAcc^ss

GFLOPs

head

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UPerNet

Mask2Former

Panoptic FPN

SETR

resolution

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512x2048

640x2560

axis

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Results

Parameters (M)

Images / Second

GFLOPs

Publication Date

Filters

Legend

Legend attribute

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Family

Pretrain Dataset

Semantic Segmentation Head

Semantic Segmentation Resolution

Semantic Segmentation Training Epochs

Legend attribute (second)

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Family

Pretrain Method

Semantic Segmentation Head

Semantic Segmentation Resolution

Semantic Segmentation Training Epochs

No Results

model	params (m)	pretrain	finetune	gflops	IN-1k
SLaK-T	50.0	IN-1k : Sup. : 300	— : — : —	8.7	82.5/—
SLaK-S	91.0	IN-1k : Sup. : 300	— : — : —	16.7	83.8/—
SLaK-B	158.0	IN-1k : Sup. : 300	— : — : —	28.5	84.0/—
SLaK-B	158.0	IN-1k : Sup. : 300	IN-1k : 30 : 384	83.8	85.5/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
SLaK-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	None	51.3	70.0	55.7	—	—	—

PASCAL VOC 2007 (test)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
SLaK-T	IN-1k : Sup. : 300	Faster R-CNN	PASCAL VOC 2007 (train + val) : 36	205.0	82.7	—	—	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
SLaK-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	None	44.3	67.2	48.1	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
SLaK-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	936.0	—	—	—	47.6	—	—
SLaK-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1028.0	—	—	—	49.4	—	—
SLaK-B	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1172.0	—	—	—	50.2	—	—