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MethodsPretraining EpochsImageNet Top-1 Accuracy(%)ADE20k mIoU(%)
Base-size models (ViT-B/16)
BEIT (Bao et al., 2022)30082.944.7
CAE (Chen et al., 2022)30083.648.3
SplitMask (El-Nouby et al., 2021)30083.645.7
MaskFeat (Wei et al., 2021)30083.6N/A
PeCo (Dong et al., 2021)30084.146.7
MVP(Wei et al., 2022)30084.452.4
iBoT (Zhou et al., 2022)40083.850.0
BEIT v2 (ours)30085.052.7
Base-size models (ViT-B/16) + pretrain longer
BEIT (Bao et al., 2022)80083.245.6
PeCo (Dong et al., 2021)80084.548.5
data2vec (Baevski et al., 2022)80084.2N/A
MAE (He et al., 2022)160083.648.1
CAE (Chen et al.,2022)160083.950.2
BEIT v2 (ours)160085.553.1
+ Intermediate fine-tuning with ImageNet-21k86.553.5
Large-size models (ViT-L/16)
iBoT (Zhou et al.,2022)25084.8N/A
MaskFeat (Wei et al., 2021)30084.4N/A
MVP (Wei et al., 2022)30086.354.3
BEIT V2 (ours)30086.655.0
Large-size models (ViT-L/16) + pretrain longer
BEIT (Bao et al., 2022)80085.253.3
MaskFeat (Wei et al., 2021)160085.7N/A
MAE (He et al., 2022)160085.953.6
CAE (Chen et al., 2022)160086.354.7
data2vec (Baevski et al.,2022)160086.6N/A
BEIT V2 (ours)160087.356.7
+ Intermediate fine-tuning with ImageNet-21k88.457.5
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MethodsImageNet AdversarialImageNet RenditionImageNet Sketch
ViT-B/16
MAE35.948.334.5
BEIT V254.461.045.6
ViT-L/16
MAE57.159.945.3
BEIT V269.069.953.5
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MethodsLinear Probe
BEIT (Bao et al., 2022)56.7
CAE (Chen et al., 2022)64.1
MAE (He et al., 2022)67.8
MVP(Wei et al., 2022)75.4
MoCo v3 (Chen et al., 2021)76.7
BEIT v2 (ours)80.1
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VQ-KD ArchitectureCodebookReconst. LossCodebook UsageImageNet Fine-tuningImageNet Linear ProbeADE20k
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Base& 6x768x120.13677%84.663.050.1
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l-th LayerHead DepthShared MIM HeadImageNet Fine-tuningImageNet Linear ProbeADE20k
1=Without patch aggregation =84.777.951.9
With patch aggregation
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92X84.879.551.9
91√84.878.951.7
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6284.977.553.1
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VQ-KD TargetsImageNetADE20k
Pretrain 300 epochs
DINO84.449.2
CLIP85.052.7
Pretrain 1600 epochs
CLIP85.553.1
Performance of VQ-KD target models
DINO83.646.8
CLIP84.9
Performance of VQ-KD encoder model
VQ-KD encoder (CLIP as target)83.6
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ModelsModel SizeLabeled Data SizeImageNet-1k 224²384²
Supervised Pretraining on ImageNet-21K
ViT-B/16 (Dosovitskiy et al.,2020)86M14M84.0
ViT-L/16 (Dosovitskiy et al., 2020)307M14M85.2
ViT-H/14 (Dosovitskiy et al., 2020)632M14M85.1
Supervised Pretraining on Google JFT-3OOM (using labeled data)
ViT-B/16 (Dosovitskiy et al., 2020)86M300M84.2
ViT-L/16 (Dosovitskiy et al., 2020)307M300M87.1
ViT-H/14 (Dosovitskiy et al., 2020)632M300M88.0
Supervised Pretraining on Google JFT-3B
ViT-B/16 (Zhai et al., 2021)86M3000M86.6
ViT-L/16 (Zhai et al., 2021)307M3000M88.5
BEIT Pretraining on ImageNet-21K, and Intermediate Fine-Tuning on ImageNet-21K
BEIT ViT-B/16 (Bao et al., 2022)86M14M85.286.8
BEIT ViT-L/16 (Bao et al.,2022)307M14M87.488.4
BEIT v2 Pretraining on ImageNet-1K, and Intermediate Fine-Tuning on ImageNet-21K
BEIT V2 ViT-B/16 (ours)86M14M86.587.5
BEIT V2 ViT-L/16 (ours)307M14M88.489.0
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HyperparametersBase SizeLarge Size
Layers Hidden size12 76824
FFN inner hidden size30721024 4096
Attention heads
1216
Layer scale Patch size0.11e-5
Relative positional embeddings16 ×16
Shared relative positional embeddings√ √
Training epochs Batch size300*/1600 2048
Adam β(0.9, 0.98*/0.999)
Peak learning rate
1.5e-3
Minimal learning rate1e-5
Learning rate scheduleCosine
Warmup epochs10
Gradient clipping3.0
DropoutX
Drop path0*/0.1
Weight decay0.05
Data AugmentRandomResizeAndCrop
Input resolution Color jitter224× 224
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HyperparametersValues
Encoder layersDecoder layersHidden sizeFFN inner hidden sizeAttention headsAttention head sizePatch sizeCodebook size12{1,3}7683072126416 ×168192 × 32
Training epochsBatch sizeAdam βPeak learning rateMinimal learning rateLearning rate scheduleWarmup epochs100512(0.9, 0.99)2e-41e-5Cosine5
Gradient clippingDropoutStoch.depthWeight decay×XX1e-4
Data AugmentInput resolutionRandomResizeAndCrop224× 224
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HyperparametersViT-B/16ViT-L/16
Peak learning rate Fine-tuning epochs Warmup epochs5e-4 1005e-4
20 0.6550 5
Layer-wise learning rate decay Batch size Adam e Adam β0.8 1024 1e-8 (0.9, 0.999)
Minimal learning rate Learning rate schedule Repeated Aug Weight decay Label smoothing ε1e-6 Cosine X 0.05 0.1
Stoch. depth Dropout Gradient clipping Erasing prob.0.1 X X 0.250.2
Input resolution Rand Augment Mixup prob. Cutmix prob. Relative positional embeddings Shared relative positional embeddings224 × 224 9/0.5 0.8 1.0 √ X
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HyperparametersViT-B/16 ViT-L/16
Input resolution512 × 512
Peak learning rateFine-tuning stepsBatch sizeAdam eAdam βLayer-wise learning rate decayMinimal learning rateLearning rate scheduleWarmup steps{0.5, 0.8, 1.0}e-4160K161e-8(0.9, 0.999){0.75, 0.8, 0.85}0Linear1500
DropoutStoch. depthWeight decayX0.1 0.20.05
Relative positional embeddingsShared relative positional embeddings√X
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