Heedless Backbones

ResNet (RSB) 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

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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JFT-3B

ImageNet-1k

ImageNet-22k

JFT-300M

pretrain method

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Supervised

FCMAE

MAE

CL

Legend

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Family

Pretrain Dataset

Pretrain Method

Classification Resolution

Legend attribute (second)

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Pretrain Dataset

Pretrain Method

Classification Resolution

model	params (m)	pretrain	finetune	GFLOPs	Top-1
ResNet-18 (RSB)	11.7	IN-1k : Sup. : 600	— : — : —	1.8	71.5
ResNet-34 (RSB)	21.8	IN-1k : Sup. : 600	— : — : —	3.7	76.4
ResNet-50 (RSB)	25.6	IN-1k : Sup. : 600	— : — : —	4.1	80.4
ResNet-101 (RSB)	44.5	IN-1k : Sup. : 600	— : — : —	7.9	81.5
ResNet-152 (RSB)	60.2	IN-1k : Sup. : 600	— : — : —	11.6	82.0

model	params (m)	pretrain	finetune	gflops	IN-1k	IN-V2	IN-ReaL
ResNet-18 (RSB)	11.7	IN-1k : Sup. : 600	— : — : —	1.8	71.5/—	59.4/—	79.4/—
ResNet-34 (RSB)	21.8	IN-1k : Sup. : 600	— : — : —	3.7	76.4/—	65.1/—	83.4/—
ResNet-50 (RSB)	25.6	IN-1k : Sup. : 600	— : — : —	4.1	80.4/—	68.7/—	85.7/—
ResNet-101 (RSB)	44.5	IN-1k : Sup. : 600	— : — : —	7.9	81.5/—	70.3/—	86.3/—
ResNet-152 (RSB)	60.2	IN-1k : Sup. : 600	— : — : —	11.6	82.0/—	70.6/—	86.4/—