A Method for Analyzing Learning Sentiment Based on Classroom Time-Series Images

Table 3

Comparison of the RUTMasking-Net model with other models.

Networks	Parameters )	Accuracy (%)
VGG19 [27]	139.5	70.80
ResNet34 [27]	27.6	72.42
EfficientNet-XGBoost [33]	—	72.54
Inception-v3 [34]	37.0	73.09
ResMaskingNet [27]	142.9	73.11
VGG [35]	143.7	73.28
STN + TL [36]	—	73.31
Cbam ResNet50 [27]	28.5	73.39
LHC-Net [37]	32.4	73.39
LHC-NetC [37]	32.4	73.53
Our RCTMasking-Net	145.1	73.58

The results of the networks presented in this article are in bold, which is used to more clearly demonstrate the advantages of the networks in this article.