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PropertiesConvolutionSelf-Attention
Translation Equivariance√
Input-adaptive Weighting√
Global Receptive Field
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MetricC-C-T-TC-T-T-T
Pre-training Precision@1 (JFT)34.4034.36
Transfer Accuracy 224x22482.3981.78
Transfer Accuracy 384x38484.2384.02
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ModelsEval Size#Params#FLOPsImageNet Top-1 Accuracy
Conv Only1K only21K+1K
EfficientNet-B7600²66M37B84.7-
EfficientNetV2-L480²121M53B85.786.8
NFNet-F34162²255M114.8B85.7=
NFNet-F55442377M289.8B86.01
ViT-Stem TFMDeiT-B384286M55.4B83.1-
ViT-L/16384²304M190.7B-85.3
CaiT-S-36384²68M48.0B85.0
DeepViT-L224²55M12.5B83.1-
Multi-stage TFMSwin-B384²88M47.0B84.286.0
Swin-L384²197M103.9B-86.4
Conv+TFMBotNet-T7384275.1M45.8B84.7-
LambdaResNet-420320²-=84.8
T2T-ViT-24224²64.1M15.0B82.6=
CvT-21384²32M24.9B83.3-
CvT-W243842277M193.2B-87.7
Conv+TFM (ours)CoAtNet-0 CoAtNet-1224²25M4.2B81.6=
224²42M8.4B83.3-
CoAtNet-2 CoAtNet-3224²75M15.7B84.187.1
2242168M34.7B84.587.6
CoAtNet-0384²25M13.4B83.9-
CoAtNet-1384242M27.4B85.1-
CoAtNet-2384²75M49.8B85.787.1
CoAtNet-3384²168M107.4B85.887.6
CoAtNet-4384²275M189.5B-87.9
+ PT-RA384²275M189.5B88.3
+ PT-RA-E1503842275M189.5B88.4
CoAtNet-2512275M96.7B85.987.3
CoAtNet-3512²168M203.1B86.087.9
CoAtNet-4512²275M360.9B-88.1
+ PT-RA512²275M360.9B=88.4
+ PT-RA-E1505122275M360.9B=88.56
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ModelsEval Size#Params#FLOPsTPUv3-core-daysTop-1 Accuracy
ResNet + ViT-L/163842330M=187.12
ViT-L/165122307M364B0.68K87.76
ViT-H/145182632M1021B2.5K88.55
NFNet-F4+5122527M367B1.86K89.2
CoAtNet-3t3842168M114B0.58K88.52
CoAtNet-3t5122168M214B0.58K88.81
CoAtNet-45122275M361B0.95K89.11
CoAtNet-55122688M812B1.82K89.77
ViT-G/1451821.84B5160B>30K90.45
CoAtNet-651221.47B1521B6.6K90.45
CoAtNet-751222.44B2586B20.1K90.88
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SetingMetricWith Rel-AttnWithout Rel-Attn
ImageNet-1KAccuracy (2242)84.183.8
Accuracy (3842)85.785.3
ImageNet-21K →ImageNet-1KPre-train Precision@1 (224²)53.052.8
Finetune Accuracy (384²)87.987.4
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SettingModelsLayoutTop-1 Accuracy
ImageNet-1KVO: CoAtNet-2[2,2,6,14，2]84.1
V1: S2← S3[2,2, 2,18,2]83.4
V2: S2→ S3[2,2,8,12,2]84.0
ImageNet-21KVO: CoAtNet-3[2,2,6,14,2]53.0 -→87.6
⇒ImageNet-1KV1: S2 ← S3[2,2,2,18,2]53.0 -→87.4
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SettingModelsImage SizeTop-1 Accuracy
ImageNet-1KCoAtNet-2224284.1
Head size: 32 → 64224283.9
Norm type: 1 BN →LN224284.1
ImageNet-21K ⇒ ImageNet-1KCoAtNet-3384287.9
Norm type: BN →→ LN384²87.8
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