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Model Zoo

ImageNet

ImageNet has multiple versions, but the most commonly used one is ILSVRC 2012. The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.

Model

Params(M)

Flops(G)

Top-1 (%)

Top-5 (%)

Config

Download

VGG-11

132.86

7.63

68.75

88.87

config

model | log

VGG-13

133.05

11.34

70.02

89.46

config

model | log

VGG-16

138.36

15.5

71.62

90.49

config

model | log

VGG-19

143.67

19.67

72.41

90.80

config

model | log

VGG-11-BN

132.87

7.64

70.75

90.12

config

model | log

VGG-13-BN

133.05

11.36

72.15

90.71

config

model | log

VGG-16-BN

138.37

15.53

73.72

91.68

config

model | log

VGG-19-BN

143.68

19.7

74.70

92.24

config

model | log

RepVGG-A0*

9.11(train) | 8.31 (deploy)

1.52 (train) | 1.36 (deploy)

72.41

90.50

config (train) | config (deploy)

model

RepVGG-A1*

14.09 (train) | 12.79 (deploy)

2.64 (train) | 2.37 (deploy)

74.47

91.85

config (train) | config (deploy)

model

RepVGG-A2*

28.21 (train) | 25.5 (deploy)

5.7 (train) | 5.12 (deploy)

76.48

93.01

config (train) | config (deploy)

model

RepVGG-B0*

15.82 (train) | 14.34 (deploy)

3.42 (train) | 3.06 (deploy)

75.14

92.42

config (train) | config (deploy)

model

RepVGG-B1*

57.42 (train) | 51.83 (deploy)

13.16 (train) | 11.82 (deploy)

78.37

94.11

config (train) | config (deploy)

model

RepVGG-B1g2*

45.78 (train) | 41.36 (deploy)

9.82 (train) | 8.82 (deploy)

77.79

93.88

config (train) | config (deploy)

model

RepVGG-B1g4*

39.97 (train) | 36.13 (deploy)

8.15 (train) | 7.32 (deploy)

77.58

93.84

config (train) | config (deploy)

model

RepVGG-B2*

89.02 (train) | 80.32 (deploy)

20.46 (train) | 18.39 (deploy)

78.78

94.42

config (train) | config (deploy)

model

RepVGG-B2g4*

61.76 (train) | 55.78 (deploy)

12.63 (train) | 11.34 (deploy)

79.38

94.68

config (train) | config (deploy)

model

RepVGG-B3*

123.09 (train) | 110.96 (deploy)

29.17 (train) | 26.22 (deploy)

80.52

95.26

config (train) | config (deploy)

model

RepVGG-B3g4*

83.83 (train) | 75.63 (deploy)

17.9 (train) | 16.08 (deploy)

80.22

95.10

config (train) | config (deploy)

model

RepVGG-D2se*

133.33 (train) | 120.39 (deploy)

36.56 (train) | 32.85 (deploy)

81.81

95.94

config (train) | config (deploy)

model

ResNet-18

11.69

1.82

70.07

89.44

config

model | log

ResNet-34

21.8

3.68

73.85

91.53

config

model | log

ResNet-50 (rsb-a1)

25.56

4.12

80.12

94.78

config

model | log

ResNet-101

44.55

7.85

78.18

94.03

config

model | log

ResNet-152

60.19

11.58

78.63

94.16

config

model | log

Res2Net-50-14w-8s*

25.06

4.22

78.14

93.85

config

model

Res2Net-50-26w-8s*

48.40

8.39

79.20

94.36

config

model

Res2Net-101-26w-4s*

45.21

8.12

79.19

94.44

config

model

ResNeSt-50*

27.48

5.41

81.13

95.59

config

model

ResNeSt-101*

48.28

10.27

82.32

96.24

config

model

ResNeSt-200*

70.2

17.53

82.41

96.22

config

model

ResNeSt-269*

110.93

22.58

82.70

96.28

config

model

ResNetV1D-50

25.58

4.36

77.54

93.57

config

model | log

ResNetV1D-101

44.57

8.09

78.93

94.48

config

model | log

ResNetV1D-152

60.21

11.82

79.41

94.7

config

model | log

ResNeXt-32x4d-50

25.03

4.27

77.90

93.66

config

model | log

ResNeXt-32x4d-101

44.18

8.03

78.71

94.12

config

model | log

ResNeXt-32x8d-101

88.79

16.5

79.23

94.58

config

model | log

ResNeXt-32x4d-152

59.95

11.8

78.93

94.41

config

model | log

SE-ResNet-50

28.09

4.13

77.74

93.84

config

model | log

SE-ResNet-101

49.33

7.86

78.26

94.07

config

model | log

RegNetX-400MF

5.16

0.41

72.56

90.78

config

model | log

RegNetX-800MF

7.26

0.81

74.76

92.32

config

model | log

RegNetX-1.6GF

9.19

1.63

76.84

93.31

config

model | log

RegNetX-3.2GF

15.3

3.21

78.09

94.08

config

model | log

RegNetX-4.0GF

22.12

4.0

78.60

94.17

config

model | log

RegNetX-6.4GF

26.21

6.51

79.38

94.65

config

model | log

RegNetX-8.0GF

39.57

8.03

79.12

94.51

config

model | log

RegNetX-12GF

46.11

12.15

79.67

95.03

config

model | log

ShuffleNetV1 1.0x (group=3)

1.87

0.146

68.13

87.81

config

model | log

ShuffleNetV2 1.0x

2.28

0.149

69.55

88.92

config

model | log

MobileNet V2

3.5

0.319

71.86

90.42

config

model | log

ViT-B/16*

86.86

33.03

85.43

97.77

config

model

ViT-B/32*

88.3

8.56

84.01

97.08

config

model

ViT-L/16*

304.72

116.68

85.63

97.63

config

model

Swin-Transformer tiny

28.29

4.36

81.18

95.61

config

model | log

Swin-Transformer small

49.61

8.52

83.02

96.29

config

model | log

Swin-Transformer base

87.77

15.14

83.36

96.44

config

model | log

Transformer in Transformer small*

23.76

3.36

81.52

95.73

config

model

T2T-ViT_t-14

21.47

4.34

81.83

95.84

config

model | log

T2T-ViT_t-19

39.08

7.80

82.63

96.18

config

model | log

T2T-ViT_t-24

64.00

12.69

82.71

96.09

config

model | log

Mixer-B/16*

59.88

12.61

76.68

92.25

config

model

Mixer-L/16*

208.2

44.57

72.34

88.02

config

model

DeiT-tiny

5.72

1.08

74.50

92.24

config

model | log

DeiT-tiny distilled*

5.72

1.08

74.51

91.90

config

model

DeiT-small

22.05

4.24

80.69

95.06

config

model | log

DeiT-small distilled*

22.05

4.24

81.17

95.40

config

model

DeiT-base

86.57

16.86

81.76

95.81

config

model | log

DeiT-base distilled*

86.57

16.86

83.33

96.49

config

model

DeiT-base 384px*

86.86

49.37

83.04

96.31

config

model

DeiT-base distilled 384px*

86.86

49.37

85.55

97.35

config

model

Conformer-tiny-p16*

23.52

4.90

81.31

95.60

config

model

Conformer-small-p32*

38.85

7.09

81.96

96.02

config

model

Conformer-small-p16*

37.67

10.31

83.32

96.46

config

model

Conformer-base-p16*

83.29

22.89

83.82

96.59

config

model

PCPVT-small*

24.11

3.67

81.14

95.69

config

model

PCPVT-base*

43.83

6.45

82.66

96.26

config

model

PCPVT-large*

60.99

9.51

83.09

96.59

config

model

SVT-small*

24.06

2.82

81.77

95.57

config

model

SVT-base*

56.07

8.35

83.13

96.29

config

model

SVT-large*

99.27

14.82

83.60

96.50

config

model

EfficientNet-B0*

5.29

0.02

76.74

93.17

config

model

EfficientNet-B0 (AA)*

5.29

0.02

77.26

93.41

config

model

EfficientNet-B0 (AA + AdvProp)*

5.29

0.02

77.53

93.61

config

model

EfficientNet-B1*

7.79

0.03

78.68

94.28

config

model

EfficientNet-B1 (AA)*

7.79

0.03

79.20

94.42

config

model

EfficientNet-B1 (AA + AdvProp)*

7.79

0.03

79.52

94.43

config

model

EfficientNet-B2*

9.11

0.03

79.64

94.80

config

model

EfficientNet-B2 (AA)*

9.11

0.03

80.21

94.96

config

model

EfficientNet-B2 (AA + AdvProp)*

9.11

0.03

80.45

95.07

config

model

EfficientNet-B3*

12.23

0.06

81.01

95.34

config

model

EfficientNet-B3 (AA)*

12.23

0.06

81.58

95.67

config

model

EfficientNet-B3 (AA + AdvProp)*

12.23

0.06

81.81

95.69

config

model

EfficientNet-B4*

19.34

0.12

82.57

96.09

config

model

EfficientNet-B4 (AA)*

19.34

0.12

82.95

96.26

config

model

EfficientNet-B4 (AA + AdvProp)*

19.34

0.12

83.25

96.44

config

model

EfficientNet-B5*

30.39

0.24

83.18

96.47

config

model

EfficientNet-B5 (AA)*

30.39

0.24

83.82

96.76

config

model

EfficientNet-B5 (AA + AdvProp)*

30.39

0.24

84.21

96.98

config

model

EfficientNet-B6 (AA)*

43.04

0.41

84.05

96.82

config

model

EfficientNet-B6 (AA + AdvProp)*

43.04

0.41

84.74

97.14

config

model

EfficientNet-B7 (AA)*

66.35

0.72

84.38

96.88

config

model

EfficientNet-B7 (AA + AdvProp)*

66.35

0.72

85.14

97.23

config

model

EfficientNet-B8 (AA + AdvProp)*

87.41

1.09

85.38

97.28

config

model

ConvNeXt-T*

28.59

4.46

82.05

95.86

config

model

ConvNeXt-S*

50.22

8.69

83.13

96.44

config

model

ConvNeXt-B*

88.59

15.36

83.85

96.74

config

model

ConvNeXt-B*

88.59

15.36

85.81

97.86

config

model

ConvNeXt-L*

197.77

34.37

84.30

96.89

config

model

ConvNeXt-L*

197.77

34.37

86.61

98.04

config

model

ConvNeXt-XL*

350.20

60.93

86.97

98.20

config

model

HRNet-W18*

21.30

4.33

76.75

93.44

config

model

HRNet-W30*

37.71

8.17

78.19

94.22

config

model

HRNet-W32*

41.23

8.99

78.44

94.19

config

model

HRNet-W40*

57.55

12.77

78.94

94.47

config

model

HRNet-W44*

67.06

14.96

78.88

94.37

config

model

HRNet-W48*

77.47

17.36

79.32

94.52

config

model

HRNet-W64*

128.06

29.00

79.46

94.65

config

model

HRNet-W18 (ssld)*

21.30

4.33

81.06

95.70

config

model

HRNet-W48 (ssld)*

77.47

17.36

83.63

96.79

config

model

WRN-50*

68.88

11.44

81.45

95.53

config

model

WRN-101*

126.89

22.81

78.84

94.28

config

model

CSPDarkNet50*

27.64

5.04

80.05

95.07

config

model

CSPResNet50*

21.62

3.48

79.55

94.68

config

model

CSPResNeXt50*

20.57

3.11

79.96

94.96

config

model

DenseNet121*

7.98

2.88

74.96

92.21

config

model

DenseNet169*

14.15

3.42

76.08

93.11

config

model

DenseNet201*

20.01

4.37

77.32

93.64

config

model

DenseNet161*

28.68

7.82

77.61

93.83

config

model

VAN-T*

4.11

0.88

75.41

93.02

config

model

VAN-S*

13.86

2.52

81.01

95.63

config

model

VAN-B*

26.58

5.03

82.80

96.21

config

model

VAN-L*

44.77

8.99

83.86

96.73

config

model

MViTv2-tiny*

24.17

4.70

82.33

96.15

config

model

MViTv2-small*

34.87

7.00

83.63

96.51

config

model

MViTv2-base*

51.47

10.20

84.34

96.86

config

model

MViTv2-large*

217.99

42.10

85.25

97.14

config

model

EfficientFormer-l1*

12.19

1.30

80.46

94.99

config

model

EfficientFormer-l3*

31.41

3.93

82.45

96.18

config

model

EfficientFormer-l7*

82.23

10.16

83.40

96.60

config

model

Models with * are converted from other repos, others are trained by ourselves.

CIFAR10

Model

Params(M)

Flops(G)

Top-1 (%)

Config

Download

ResNet-18-b16x8

11.17

0.56

94.82

config

ResNet-34-b16x8

21.28

1.16

95.34

config

ResNet-50-b16x8

23.52

1.31

95.55

config

ResNet-101-b16x8

42.51

2.52

95.58

config

ResNet-152-b16x8

58.16

3.74

95.76

config

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