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领域套件与扩展包

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。

yolo

model

map

mindyolo recipe

vanilla mindspore

yolov8_n

37.2

config

yolov8_s

44.6

config

yolov8_m

50.5

config

yolov8_l

52.8

config

yolov8_x

53.7

config

yolov7_t

37.5

config

yolov7_l

50.8

config

yolov7_x

52.4

config

yolov5_n

27.3

config

yolov5_s

37.6

config

link

yolov5_m

44.9

config

yolov5_l

48.5

config

yolov5_x

50.5

config

yolov4_csp

45.4

config

yolov4_csp(silu)

45.8

config

link

yolov3_darknet53

45.5

config

link

yolox_n

24.1

config

yolox_t

33.3

config

yolox_s

40.7

config

yolox_m

46.7

config

yolox_l

49.2

config

yolox_x

51.6

config

yolox_darknet53

47.7

config

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

fasterrcnn

58

link

link

maskrcnn_mobilenetv1

coming soon

link

maskrcnn_resnet50

coming soon

link

语义分割

model

mind_series recipe

vanilla mindspore

ocrnet

link

link

deeplab v3

link

deeplab v3 plus

link

unet

link

OCR

文本检测

model

dataset

fscore

mindocr recipe

vanilla mindspore

dbnet_mobilenetv3

icdar2015

77.23

config

link

dbnet_resnet18

icdar2015

81.73

config

link

dbnet_resnet50

icdar2015

85.05

config

link

dbnet++_resnet50

icdar2015

86.74

config

psenet_resnet152

icdar2015

82.06

config

link

east_resnet50

icdar2015

84.87

config

link

fcenet_resnet50

icdar2015

84.12

config

文本识别

model

dataset

acc

mindocr recipe

vanilla mindspore

svtr_tiny

IC03,13,15,IIIT,etc

89.02

config

crnn_vgg7

IC03,13,15,IIIT,etc

82.03

config

link

crnn_resnet34_vd

IC03,13,15,IIIT,etc

84.45

config

rare_resnet34_vd

IC03,13,15,IIIT,etc

85.19

config

link

文本方向分类

model

dataset

acc

mindocr recipe

mobilenetv3

RCTW17,MTWI,LSVT

94.59

config

人脸

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

语言模型

model

mindformer recipe

vanilla mindspore

bert_base

config

link

t5_small

config

gpt2_small

config

gpt2_13b

config

gpt2_52b

config

pangu_alpha

config

glm_6b

config

glm_6b_lora

config

llama_7b

config

llama_13b

config

llama_65b

config

llama_7b_lora

config

bloom_560m

config

bloom_7.1b

config

bloom_65b

config

bloom_176b

config

强化学习

Algorithm

Discrete Action Space

Continuous Action Space

CPU

GPU

Ascend

Environment

Reward

DQN

/

CartPole-v0

195

PPO

/

HalfCheetah-v2

4800

AC

/

CartPole-v0

195

A2C

/

CartPole-v0

195

DDPG

/

HalfCheetah-v2

4800

QMIX

/

SMAC/Simple Spread

90%/-145

SAC

/

HalfCheetah-v2

4800

TD3

/

HalfCheetah-v2

4800

C51

/

CartPole-v0

195

A3C

/

/

CartPole-v0

195

CQL

/

Hopper-v0

3500

MAPPO

/

Simple Spread

-145

GAIL

/

HalfCheetah-v2

4800

AWAC

/

Ant-v2

5000

Dreamer

/

/

Walker-walk

900

IQL

/

Walker2d-v2

3000

MADDPG

/

simple_spread

-140

Double DQN

/

CartPole-v0

195

Policy Gradient

/

CartPole-v0

195

Dueling DQN

/

CartPole-v0

195

科学计算套件

领域

网络

MindSpore实现

Ascend

GPU

通用物理

auq_pinns

Link

通用物理

cpinns

Link

通用物理

deep_hpms

Link

通用物理

deep_ritz

Link

通用物理

deepbsde

Link

通用物理

deeponet

Link

通用物理

dgm

Link

通用物理

fbsnns

Link

通用物理

fpinns

Link

通用物理

gradient_pathologies_pinns

Link

通用物理

hp_vpinns

Link

通用物理

laaf

Link

通用物理

mgnet

Link

通用物理

multiscale_pinns

Link

通用物理

pfnn

Link

通用物理

phygeonet

Link

通用物理

pi_deeponet

Link

通用物理

pinns

Link

通用物理

pinns_ntk

Link

通用物理

ppinns

Link

通用物理

xpinns

Link

哈密顿系统

sympnets

Link

弹性动力学

pinn_elastodynamics

Link

热力学

pinn_heattransfer

Link

气象学

enso

Link

地质学

inversion_net

Link

地质学

pinn_helmholtz

Link

海洋物理

ocean_model

Link

海洋物理

pinns_swe

Link

电磁学

meta_auto_decoder

Link

电磁学

pinn_fwi

Link

电磁学

time_domain_maxwell

Link

电磁学

frequency_domain_maxwell

Link

电磁学

AD_FDTD

Link

电磁学

SED_ANN

Link

电磁学

Metasurface_holograms

Link

电磁学

maxwell_net

Link

计算生物

MEGA-Fold

Link

计算生物

MEGA-EvoGen

Link

计算生物

MEGA-Assessment

Link

计算生物

ColabDesign

Link

计算生物

DeepFRI

Link

计算生物

FAAST

Link

计算生物

JT-VAE

Link

计算生物

MG-BERT

Link

计算生物

Multimer

Link

计算生物

ProteinMPNN

Link

计算生物

UFold

Link

计算生物

ESM-IF1

Link

计算生物

ESM2

Link

计算生物

Grover

Link

计算生物

Pafnucy

Link

计算流体

FNO1D

Link

计算流体

KNO1D

Link

计算流体

FNO2D

Link

计算流体

KNO2D

Link

计算流体

FNO3D

Link

计算流体

CAE-LSTM

Link

计算流体

eHDNN

Link

计算流体

HDNN

Link

计算流体

ViT

Link

计算流体

PeRCNN

Link

计算流体

Burgers1D

Link

计算流体

Cylinder Flow

Link

计算流体

PDE-Net

Link

计算流体

hfm

Link

计算流体

label_free_dnn_surrogate

Link

计算流体

nsf_nets

Link

大模型套件

Transformers

model

dataset

metric

score

mindformers config

bert_base_uncased

wiki

-

-

config

bert_tiny_uncased

wiki

-

-

config

qa_bert_base_uncased

SQuAD v1.1

EM / F1

80.74 / 88.33

config

tokcls_bert_base_chinese_cluener

CLUENER

Entity F1

0.7905

config

txtcls_bert_base_uncased_mnli

Mnli

Entity F1

84.80%

config

clip_vit_b_32

Cifar100

Accuracy

57.24%

config

clip_vit_b_16

Cifar100

Accuracy

61.41%

config

clip_vit_l_14

Cifar100

Accuracy

69.67%

config

clip_vit_l_14@336

Cifar100

Accuracy

68.19%

config

glm_6b

ADGEN

BLEU-4 / Rouge-1 / Rouge-2 / Rouge-l

8.42 / 31.75 / 7.98 / 25.28

config

gpt2

wikitext-2

-

-

config

gpt2_13b

wikitext-2

-

-

config

gpt2_52b

wikitext-2

-

-

config

llama_7b

alpac

-

-

config

llama_13b

alpac

-

-

config

mae_vit_base_p16

ImageNet-1K

-

-

config

vit_base_p16

ImageNet-1K

Accuracy

83.71%

config

pangualpha_2_6b

悟道数据集

-

-

config

pangualpha_13b

悟道数据集

-

-

config

swin_base_p4w7

ImageNet-1K

Accuracy

83.44%

config

t5_small

WMT16

-

-

config

t5_tiny

WMT16

-

-

config

推荐

model

dataset

auc

mindrec recipe

vanilla mindspore

Wide&Deep

Criteo

0.8

link

link

Deep&Cross Network (DCN)

Criteo

0.8

link

link