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Models PT Epochs Top-1 ViT-Small/16 model results
Scratch (Touvron et al., 2021a) MoCo-v3† (Chen et al., 2021) 600 DINO (Caron et al., 2021) 1600 81.5 BEiT (Bao et al., 2021) 300
CIM-RESPIX (Ours) 300 81.5 CIM-REVDET (Ours) 300 81.6
ViT-Base/l6 model results 81.8
Scratch (Touvron et al., 2021a)
Scratch (He et al., 2021) 82.3
DINO (Caron et al., 2021) 1600 82.8 MoCo-v3 (Chen et al.,2021) 600 83.2
BEiT (Bao et al.,2021) 300 82.9 BEiT (Bao et al., 2021) 800 83.2
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ModelsPT Epochs Top-1
Fine-tuning for l0o epochs RSB A3 (Wightman et al., 2021) CIM-REVDET (Ours) 30078.1 78.8
Fine-tuning for 300 epochs RSB A2 (Wightman et al., 2021) SimSiam (Chen & He,2021) MoCo-v2 (Chen et al., 2020c) SimCLR (Chen et al., 2020b) SimCLR (Chen et al., 2020b) BYOL (Grill et al., 2020) SwAV (Caron et al., 2020) CIM-RESPIX(Ours) CIM-REVDET (Ours)79.8 400 79.1 400 79.6 800 79.9 2000 80.0 400 80.0 600 80.1 300 79.9 300
Fine-tuning for 60o epochs80.5
RSB A1 (Wightman et al., 2021) CIM-REVDET (Ours) 30080.4 80.7
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Models PT Epochs mloU
Fine-tuning for 80k iterations Training from Scratch 29.9 IN1K Supervised† (He et al., 2019) 120 35.9 CIM-REVDET (Ours)
300 36.2 Fine-tuning forl6Ok iterations Training from Scratch 36.7 IN1K Supervised (He et al., 2019) 120 36.1 BYOL (Grill et al., 2020) 400 37.1
SimCLR (Chen et al.,2020b) 2000 37.7 CIM-RESPIX (Ours) 300 38.7
SimSiam (Chen & He,2021) 400 37.1 SwAV (Caron et al., 2020) 600 37.2 MoCo-v2 (Chen et al.,2020c) 400 37.5 SimCLR (Chen et al., 2020b) 800 37.6
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Models PT Epochs Top-1
Fine-tuning for l60k iterations
DINO (Caron et al., 2021) 1600 43.0
BEiT (Bao et al., 2021) 300 43.2
CIM-RESPIX (Ours) 300 43.5
CIM-REVDET (Ours) 300 43.6
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Masking StrategyMasking RatioTop-1 Acc.
Blockwise40%82.8
Blockwise50%82.9
Blockwise60%82.8
Random40%83.0
Random50%83.3
Random60%83.1
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# Enc.LayersWeight SharingTop-1 Acc.
4X83.1
4√83.3
5√83.2
6√83.2
7√83.1
8√82.9
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MethodsPT Epochs FT Epochs Top-1 Acc.
ResNet-50x2 (#params: 94M)
From Scratch=40081.1
SimCLR (Chen et al., 2020b)1000100 /20081.6 /82.1
CIM-REVDET (Ours)300100/20081.7 /82.2
ResNet-50x4 (#params:375M)
From Scratch40080.9
SimCLR (Chen et al., 2020b)100010082.6
SimMIM (Xie et al.,2021)30010081.6
CIM-REVDET (Ours)30010082.6
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Sampling StrategyTop-1 Acc.
Uniform sampling77.2
argmax sampling78.5
softmax sampling83.3
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REsPIX Recon. TargetTop-1 Acc.
w/o norm.82.8
norm.w/ non-overlap win.83.0
norm. w/ sliding win.83.3
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MethodsTop-1 Acc.
ViT-Large (#params: 304M)
From Scratch (He et al., 2021)82.6
MoCo-v3 (Chen et al., 2021)84.1
BEiT (Bao et al., 2021)85.2
CIM-RESPIX (Ours)84.3
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BEiT-Style generator w/ VQGAN tokenizer82.9 (-0.4)
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MethodsPT EpochsRelative PT TimeTop-1 Acc.
BEiT3001.0x82.9
BEiT8002.7x83.2
BEiT16005.3x83.3
CIM8001.8x83.3
CIM8004.8x83.4
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MethodsPT EpochsRelative PT Time Top-1 Acc.
SimMIM3001.0x81.6
CIM1000.9x82.2
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Pre-training Config. (ViT & ResNet)Value
OptimizerAdamW (Loshchilov & Hutter, 2017)
Pre-training Epochs300
Peak Learning Rate1.5e-3
Batch Size2048
Weight Decay0.05
Optimizer Momentum (β1,β2)(0.9, 0.98) (Vaswani et al., 2017)
Learning Rate ScheduleCosine Decay
Gradient Clipping3.0
Warmup Epochs10
#Masked Patches for the Generator100 to 120, Random Masking
The Generator's Depth4 to 6
The Generator's WidthSame to the Enhancer (ViT), 384 (ResNet)
The Enhancer's Loss Weight1 forREVDET,1O forREsPIX
Data AugmentationRandomResizedCrop Only
Dropout (Srivastava et al., 2014)X X
Stochastic Depth (Huang et al., 2016)
LayerScale (Touvron et al., 2021b) Pos.Emb.in TransformerLayersX
1-D Absolute Pos. Emb. (Dosovitskiy et al., 2020)
Patch Size16 224
Pre-training Resolution
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Fine-tuning Config. (ViT)Value
OptimizerAdamW (Loshchilov & Hutter, 2017)
Fine-tuning Epochs200 forViT-S/16,10O forViT-B/16
Peak Learning Rate3e-3 forViT-B/16REsPIX,5e-3 forViT-B/16 REVDET,3e-3 or4e-3 forViT-S/16
Layer-wise Learning Rate Decay (Bao et al.,0.8 (Clark et al., 2020)
2021) Batch Size1024
Weight Decay0.05
Optimizer Momentum (β1, β2)(0.9, 0.999)
Learning Rate ScheduleCosine Decay
Warmup Epochs5
Gradient ClippingX
Dropout (Srivastava et al., 2014)X
Stochastic Depth (Huang et al., 2016)0.1
Label Smoothing (Szegedy et al., 2016)0.1
Mixup (Zhang et al., 2017)0.8
CutMix (Yun et al., 2019)1.0
Random Augmentation (Cubuk et al., 2020)9 /0.5
Patch Size16
Fine-tuning Resolution224
Test Resolution224
Test Crop Ratio0.95
Loss FunctionCross Entropy Loss
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Fine-tuning Config. (ResNet-50)100 Epoch FT300Epoch FT600 Epoch FT
OptimizerAdamW (Loshchilov & Hutter, 2017)
Peak Learning Rate12e-3
Layer-wise Learning Rate Decay (Bao et al., 2021)X
Batch Size2048
Learning Rate ScheduleCosine Decay
Loss FunctionBinary Cross Entropy Loss
Warmup Epochs5
Weight Decay0.020.020.01
Fine-tuning Resolution160224224
Test Resolution224
Test Crop Ratio0.95
Repeated Augmentation (Berman et al.,X√1
2019; Hoffer et al., 2019) Random Augmentation (Cubuk et al.,6/0.57 /0.57 /0.5
2020)
Mixup (Zhang et al., 2017) CutMix (Yun et al., 2019)0.10.1 1.00.2
Label Smoothing (Szegedy et al., 2016)0.10.1
Stochastic Depth (Huang et al.,2016)X
XX0.05
Dropout (Srivastava et al., 2014)X
Layer-wise Learning Rate DecayX
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