Heedless Backbones

WTConvNeXt 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)

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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

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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
WTConvNeXt-T	30.0	IN-1k : Sup. : 300	— : — : —	4.5	82.5
WTConvNeXt-S	54.0	IN-1k : Sup. : 300	— : — : —	8.8	83.6
WTConvNeXt-B	93.0	IN-1k : Sup. : 300	— : — : —	15.5	84.1

model	params (m)	pretrain	finetune	gflops	IN-1k	IN-C↓	IN-C-bar↓	IN-A	IN-R	IN-Sketch
WTConvNeXt-T	30.0	IN-1k : Sup. : 300	— : — : —	4.5	82.5/—	52.0/—	38.0/—	25.3/—	48.0/—	35.8/—
WTConvNeXt-S	54.0	IN-1k : Sup. : 300	— : — : —	8.8	83.6/—	—/—	—/—	—/—	—/—	—/—
WTConvNeXt-B	93.0	IN-1k : Sup. : 300	— : — : —	15.5	84.1/—	45.6/—	32.2/—	36.9/—	52.6/—	39.9/—

COCO (val)

model	pretrain	head	train	gflops	mAP^b	AP^b₅₀	AP^b₇₅	mAP^b_s	mAP^b_m	mAP^b_l
WTConvNeXt-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	741.0	51.0	69.9	55.3	—	—	—
WTConvNeXt-S	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	827.0	52.6	71.5	57.1	—	—	—

COCO (val)

model	pretrain	head	train	gflops	mAP^m	AP^m₅₀	AP^m₇₅	mAP^m_s	mAP^m_m	mAP^m_l
WTConvNeXt-T	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	741.0	44.4	67.2	48.1	—	—	—
WTConvNeXt-S	IN-1k : Sup. : 300	Cascade Mask R-CNN	COCO (train) : 36	827.0	45.6	69.0	49.5	—	—	—

ADE20K (val)

model	pretrain	head	train	gflops	mIoU^ms	pAcc^ms	mAcc^ms	mIoU^ss	pAcc^ss	mAcc^ss
WTConvNeXt-T	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	939.0	—	—	—	46.6	—	—
WTConvNeXt-S	IN-1k : Sup. : 300	UPerNet	ADE20K (train) : 128 : 512	1028.0	—	—	—	49.0	—	—