Table 1.
Comparative summary of the studies found in the literature based on face mask detection.
| Model | The Number of classes | The Number of images | Classification Accuracy (%) | References |
|---|---|---|---|---|
| MobileNetMask | 2 | 3835 1376 |
93 100 |
[16] |
| YOLOv3 | 2 | 600 | 96 | [17] |
| ResNet50 + SVM | 2 | 15,000 1570 13,000 |
between 92 and 98 | [18] |
| CNN | 2 | 1376 | 98 | [19] |
| CNN | 2 | 135,849 | 99.83 | [20] |
| CNN | 2 | 1539 | 98.7 | [1] |
| R-CNN Inception ResNet V2 | 2 | 1853 | 99.8 | [21] |
| ResNet | 2 | 95,000 | 97 | [22] |
| MobilNetV2 + SVM | 2 | 1376 | 97.1 | [4] |
| YoloV2 + ResNet-50 | 2 | 1415 | 81 (Precision) | [23] |
| MobileNet V2 | 2 | 3165 | between 85 and 95 | [24] |
| VGG-16 | 2 | 20,000 | 97 | [13] |
| MobileNetV2 | 2 | 3846 | 96.85 | [26] |
| InceptionV3 | 2 | 1570 | 100 | [27] |
| Yolo + CNN | 2 | – | 95 | [28] |
| DenseNet-121 | 2 | 7855 | 99.49 | [29] |
| CNN | 2 | 95,000 24,771 500,000 65,617 |
97 | [12] |
| YoloV3 NASNetMobile |
3 2 |
680 1400 |
98 99 |
[15] |
| VGG-16 | 2 | 25,000 | 96 | [30] |
| MobileNet | 2 | 3216 | 94.2 | [31] |