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Image Classification on ImageNet 1k (val) (Linear & k-NN Probing)

82.2Linear Accuracy

MAE-CT_aug

Updated 3d ago

Evaluation Results

Method	Links
MAE-CT_aug 2023.04		82.2	79.8
MAE-CT_min 2023.04		81.5	79.4
MAE-CT_aug 2023.04		81.5	79.1
Supervised 2021.06		81.2	-
I-JEPA 2023.04		81.1	70.1
MoCo v3 2023.04		81	-
iBOT 2023.04		81	78
Layer Grafting 2023.04		81	77.3
DINO + XCiT-M24/8 \| 384 2021.06		80.9	-
MSN 2023.04		80.7	-
DINO + XCiT-M24/8 2021.06		80.3	77.9
MAE-CT_min 2023.04		80.2	78
DINO + ViT-B/8 2021.06		80.1	77.4
DINO 2023.04		80.1	77.4
iBOT 2023.04		79.5	77.1
I-JEPA 2023.04		79.3	71.6
DINO + ViT-S/8 2021.06		79.2	77.2
DINO + XCiT-S12/8 2021.06		79.2	77.1
MoCo v3 2023.04		79.1	-
DINO + XCiT-M24/16 2021.06		78.8	76.4
DINO 2023.04		78.3	-
DINO + ViT-B/16 2021.06		78.2	76.1
MoCo v3 2023.04		78.1	-
MAE (reimpl.) 2023.04		78	61.1
Layer Grafting 2023.04		77.9	75.4
DINO + XCiT-S12/16 2021.06		77.8	76
MoCo v3 2023.04		77.6	-
I-JEPA 2023.04		77.5	-
MAE 2023.04		77.2	58.9
DINO 2021.06		77	74.5
MST 2021.06		76.9	75
MAE-CT_aug 2023.04		76.9	73.4
MoCo v3 2023.04		76.7	72.6
Supervised 2021.06		76.5	-
NNCLR 2023.04		76.5	-
Supervised 2021.06		76.4	-
MST 2021.06		76.3	75
DINO + ViT-S/16 2021.06		76.1	72.8
DINO 2023.04		76.1	-
MAE 2023.04		76	60.6
DINO 2021.06		75.9	72.8
MAE (reimpl.) 2023.04		75.9	57.6
SwAV 2021.06		75.3	65.7
DINO 2021.06		75.2	72.8
MoBY + Swin-T 2021.06		75	-
MST 2021.06		75	72.1
DINO + ResNet-50 2021.06		74.5	65.6
BYOL 2021.06		74.4	64.8
CMAE 2023.04		73.9	-
MST 2021.06		73.8	66.2
SwAV 2021.06		73.5	66.3
MAE-CT_min 2023.04		73.5	64.1
I-JEPA 2023.04		72.9	-
MOBY 2021.06		72.8	-
MoCov2 2021.06		72.7	64.4
MoCov3 2021.06		72.5	-
BYOL 2021.06		71.4	66.6
MoCov2 2021.06		71.1	61.9
BYOL 2021.06		71	-
MOBY 2021.06		70.9	57.34
SimCLR 2021.06		69	-
MAE 2023.04		68	50
SwAV 2021.06		67.1	-
MAE (reimpl.) 2023.04		66.7	51.1
SimMIM 2023.04		56.7	-