Image Classification Model

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Image classification introduction

Image classification is to identity what an image represents, to predict the object list and the probabilities. For example,the following table shows the classification results after mode inference.

image_classification

Category

Probability

plant

0.9359

flower

0.8641

tree

0.8584

houseplant

0.7867

Using MindSpore Lite to realize image classification example.

Image classification model list

The following table shows the data of some image classification models using MindSpore Lite inference.

The performance of the table below is tested on the mate30.

Model name

Size(Mb)

Top1

Top5

F1

CPU 4 thread delay (ms)

MobileNetV2

11.5

-

-

65.5%

14.595

Inceptionv3

90.9

78.62%

94.08%

-

92.086

Shufflenetv2

8.8

67.74%

87.62%

-

8.303

GoogleNet

25.3

72.2%

90.06%

-

23.257

ResNext50

95.8

73.1%

91.21%

-

138.164

GhostNet

15.0

73.9%

91.40%

-

9.959

GhostNet600

40.4

80.2%

94.90%

-

52.243

GhostNet_int8

15.3

73.6%

-

-

31.452

VGG-Small-low_bit

17.8

93.7%

-

-

9.082

ResNet50-0.65x

48.6

80.2%

-

-

89.816

plain-CNN-ResNet18

97.3

95.4%

-

-

63.227

plain-CNN-ResNet34

80.5

95.0%

-

-

20.652

plain-CNN-ResNet50

89.6

94.5%

-

-

24.561