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计算机视觉

图像分类(骨干类)

以下数据基于Ascend 910环境和ImageNet-1K数据集获得。

model

acc@1

mindcv recipe

vanilla mindspore

vgg11

71.86

config

vgg13

72.87

config

vgg16

74.61

config

link

vgg19

75.21

config

link

resnet18

70.21

config

link

resnet34

74.15

config

link

resnet50

76.69

config

link

resnet101

78.24

config

link

resnet152

78.72

config

link

resnetv2_50

76.90

config

resnetv2_101

78.48

config

dpn92

79.46

config

dpn98

79.94

config

dpn107

80.05

config

dpn131

80.07

config

densenet121

75.64

config

densenet161

79.09

config

densenet169

77.26

config

densenet201

78.14

config

seresnet18

71.81

config

seresnet34

75.36

config

seresnet50

78.31

config

seresnext26

77.18

config

seresnext50

78.71

config

skresnet18

73.09

config

skresnet34

76.71

config

skresnet50_32x4d

79.08

config

resnext50_32x4d

78.53

config

resnext101_32x4d

79.83

config

resnext101_64x4d

80.30

config

resnext152_64x4d

80.52

config

rexnet_x09

77.07

config

rexnet_x10

77.38

config

rexnet_x13

79.06

config

rexnet_x15

79.94

config

rexnet_x20

80.64

config

resnest50

80.81

config

resnest101

82.50

config

res2net50

79.35

config

res2net101

79.56

config

res2net50_v1b

80.32

config

res2net101_v1b

95.41

config

googlenet

72.68

config

inceptionv3

79.11

config

link

inceptionv4

80.88

config

link

mobilenet_v1_025

53.87

config

mobilenet_v1_050

65.94

config

mobilenet_v1_075

70.44

config

mobilenet_v1_100

72.95

config

mobilenet_v2_075

69.98

config

mobilenet_v2_100

72.27

config

mobilenet_v2_140

75.56

config

mobilenet_v3_small

68.10

config

mobilenet_v3_large

75.23

config

link

shufflenet_v1_g3_x0_5

57.05

config

shufflenet_v1_g3_x1_5

67.77

config

link

shufflenet_v2_x0_5

57.05

config

shufflenet_v2_x1_0

67.77

config

link

shufflenet_v2_x1_5

57.05

config

shufflenet_v2_x2_0

67.77

config

xception

79.01

config

link

ghostnet_50

66.03

config

ghostnet_100

73.78

config

ghostnet_130

75.50

config

nasnet_a_4x1056

73.65

config

mnasnet_0.5

68.07

config

mnasnet_0.75

71.81

config

mnasnet_1.0

74.28

config

mnasnet_1.4

76.01

config

efficientnet_b0

76.89

config

link

efficientnet_b1

78.95

config

link

efficientnet_b2

79.80

link

efficientnet_b3

80.50

link

efficientnet_v2

83.77

link

regnet_x_200mf

68.74

config

regnet_x_400mf

73.16

config

regnet_x_600mf

73.34

config

regnet_x_800mf

76.04

config

regnet_y_200mf

70.30

config

regnet_y_400mf

73.91

config

regnet_y_600mf

75.69

config

regnet_y_800mf

76.52

config

mixnet_s

75.52

config

mixnet_m

76.64

config

mixnet_l

78.73

config

hrnet_w32

80.64

config

hrnet_w48

81.19

config

bit_resnet50

76.81

config

bit_resnet50x3

80.63

config

bit_resnet101

77.93

config

repvgg_a0

72.19

config

repvgg_a1

74.19

config

repvgg_a2

76.63

config

repvgg_b0

74.99

config

repvgg_b1

78.81

config

repvgg_b2

79.29

config

repvgg_b3

80.46

config

repvgg_b1g2

78.03

config

repvgg_b1g4

77.64

config

repvgg_b2g4

78.80

config

repmlp_t224

76.71

config

convnext_tiny

81.91

config

convnext_small

83.40

config

convnext_base

83.32

config

vit_b_32_224

75.86

config

link

vit_l_16_224

76.34

config

vit_l_32_224

73.71

config

swintransformer_tiny

80.82

config

link

pvt_tiny

74.81

config

pvt_small

79.66

config

pvt_medium

81.82

config

pvt_large

81.75

config

pvt_v2_b0

71.50

config

pvt_v2_b1

78.91

config

pvt_v2_b2

81.99

config

pvt_v2_b3

82.84

config

pvt_v2_b4

83.14

config

pit_ti

72.96

config

pit_xs

78.41

config

pit_s

80.56

config

pit_b

81.87

config

coat_lite_tiny

77.35

config

coat_lite_mini

78.51

config

coat_tiny

79.67

config

convit_tiny

73.66

config

convit_tiny_plus

77.00

config

convit_small

81.63

config

convit_small_plus

81.80

config

convit_base

82.10

config

convit_base_plus

81.96

config

crossvit_9

73.56

config

crossvit_15

81.08

config

crossvit_18

81.93

config

mobilevit_xx_small

68.90

config

mobilevit_x_small

74.98

config

mobilevit_small

78.48

config

visformer_tiny

78.28

config

visformer_tiny_v2

78.82

config

visformer_small

81.76

config

visformer_small_v2

82.17

config

edgenext_xx_small

71.02

config

edgenext_x_small

75.14

config

edgenext_small

79.15

config

edgenext_base

82.24

config

poolformer_s12

77.33

config

xcit_tiny_12_p16

77.67

config

目标检测

以下数据基于Ascend 910环境和COCO2017数据集获得。

经典

model

map

mind_series recipe

vanilla mindspore

ssd_vgg16

23.2

link

ssd_mobilenetv1

22.0

link

ssd_mobilenetv2

29.1

link

ssd_resnet50

34.3

link

fastrcnn

58

link

maskrcnn_mobilenetv1

coming soon

link

maskrcnn_resnet50

coming soon

link

语义分割

model

mind_series recipe

vanilla mindspore

ocrnet

link

deeplab v3

link

deeplab v3 plus

link

unet

link

OCR

model

dataset

fscore

mindocr recipe

vanilla mindspore

dbnet_mobilenetv3

icdar2015

77.28

config

dbnet_resnet18

icdar2015

83.71

config

dbnet_resnet50

icdar2015

84.99

config

link

dbnet_resnet50

msra-td500

85.03

config

dbnet++_resnet50

icdar2015

86.60

config

psenet_resnet152

icdar2015

82.06

config

link

east_resnet50

icdar2015

84.87

config

link

svtr_tiny

IC03,13,15,IIT,etc

89.02

config

crnn_vgg7

IC03,13,15,IIT,etc

82.03

config

link

crnn_resnet34_vd

IC03,13,15,IIT,etc

84.45

config

rare_resnet34_vd

IC03,13,15,IIT,etc

85.19

config

Face

model

dataset

acc

mindface recipe

vanilla mindspore

arcface_mobilefacenet-0.45g

MS1MV2

98.70

config

arcface_r50

MS1MV2

99.76

config

arcface_r100

MS1MV2

99.38

config

link

arcface_vit_t

MS1MV2

99.71

config

arcface_vit_s

MS1MV2

99.76

config

arcface_vit_b

MS1MV2

99.81

config

arcface_vit_l

MS1MV2

99.75

config

retinaface_mobilenet_0.25

WiderFace

90.77/88.2/74.76

config

link

retinaface_r50

WiderFace

95.07/93.61/84.84

config

link