Image Classification Model Support (Lite)

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.

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