Heedless Backbones

IdentityFormer 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

axis

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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

Legend attribute

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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
IdentityFormer-S12	11.9	IN-1k : Sup. : 300	— : — : —	1.8	74.6
IdentityFormer-S24	21.3	IN-1k : Sup. : 300	— : — : —	3.4	78.2
IdentityFormer-S36	30.8	IN-1k : Sup. : 300	— : — : —	5.0	79.3
IdentityFormer-M36	56.1	IN-1k : Sup. : 300	— : — : —	8.8	80.0
IdentityFormer-M48	73.3	IN-1k : Sup. : 300	— : — : —	11.5	80.4

model	params (m)	pretrain	finetune	gflops	IN-1k
IdentityFormer-S12	11.9	IN-1k : Sup. : 300	— : — : —	1.8	74.6/—
IdentityFormer-S24	21.3	IN-1k : Sup. : 300	— : — : —	3.4	78.2/—
IdentityFormer-S36	30.8	IN-1k : Sup. : 300	— : — : —	5.0	79.3/—
IdentityFormer-M36	56.1	IN-1k : Sup. : 300	— : — : —	8.8	80.0/—
IdentityFormer-M48	73.3	IN-1k : Sup. : 300	— : — : —	11.5	80.4/—