The Squeeze and Excitation (SE) Module [1].
| Basic CNN | SENet (CNN + SE Module) | |
|---|---|---|
| Accuracy | 0.99340 | 0.99350 |
| Precision | 0.99339 | 0.99344 |
| Recall | 0.99329 | 0.99342 |
| F1-Score | 0.99334 | 0.99342 |
Confusion Matrix
[[ 979 0 0 0 0 0 0 1 0 0]
[ 0 1132 0 1 0 0 1 1 0 0]
[ 0 0 1029 0 0 0 0 3 0 0]
[ 0 0 1 1006 0 3 0 0 0 0]
[ 0 0 1 0 975 0 2 0 0 4]
[ 1 0 0 7 0 882 1 0 0 1]
[ 4 2 0 0 0 1 950 0 1 0]
[ 1 3 3 2 0 0 0 1018 1 0]
[ 3 0 1 1 0 1 0 0 966 2]
[ 0 0 0 1 6 2 0 3 0 997]]
Class-0 | Precision: 0.99089, Recall: 0.99898, F1-Score: 0.99492
Class-1 | Precision: 0.99560, Recall: 0.99736, F1-Score: 0.99648
Class-2 | Precision: 0.99420, Recall: 0.99709, F1-Score: 0.99565
Class-3 | Precision: 0.98821, Recall: 0.99604, F1-Score: 0.99211
Class-4 | Precision: 0.99388, Recall: 0.99287, F1-Score: 0.99338
Class-5 | Precision: 0.99213, Recall: 0.98879, F1-Score: 0.99045
Class-6 | Precision: 0.99581, Recall: 0.99165, F1-Score: 0.99372
Class-7 | Precision: 0.99220, Recall: 0.99027, F1-Score: 0.99124
Class-8 | Precision: 0.99793, Recall: 0.99179, F1-Score: 0.99485
Class-9 | Precision: 0.99303, Recall: 0.98811, F1-Score: 0.99056
Total | Accuracy: 0.99340, Precision: 0.99339, Recall: 0.99329, F1-Score: 0.99334
Confusion Matrix
[[ 978 1 0 0 0 0 0 1 0 0]
[ 0 1134 1 0 0 0 0 0 0 0]
[ 1 1 1024 0 0 0 0 5 0 1]
[ 0 0 0 1005 0 2 0 1 2 0]
[ 0 0 2 0 976 0 2 0 0 2]
[ 1 0 0 4 0 885 1 0 0 1]
[ 3 2 0 0 2 1 949 0 1 0]
[ 0 3 4 0 0 0 0 1020 0 1]
[ 2 0 1 0 1 1 0 0 966 3]
[ 0 0 0 0 4 2 0 3 0 1000]]
Class-0 | Precision: 0.99289, Recall: 0.99796, F1-Score: 0.99542
Class-1 | Precision: 0.99387, Recall: 0.99912, F1-Score: 0.99649
Class-2 | Precision: 0.99225, Recall: 0.99225, F1-Score: 0.99225
Class-3 | Precision: 0.99604, Recall: 0.99505, F1-Score: 0.99554
Class-4 | Precision: 0.99288, Recall: 0.99389, F1-Score: 0.99338
Class-5 | Precision: 0.99327, Recall: 0.99215, F1-Score: 0.99271
Class-6 | Precision: 0.99685, Recall: 0.99061, F1-Score: 0.99372
Class-7 | Precision: 0.99029, Recall: 0.99222, F1-Score: 0.99125
Class-8 | Precision: 0.99690, Recall: 0.99179, F1-Score: 0.99434
Class-9 | Precision: 0.99206, Recall: 0.99108, F1-Score: 0.99157
Total | Accuracy: 0.99370, Precision: 0.99373, Recall: 0.99361, F1-Score: 0.99367
- Python 3.6.8
- Tensorflow 1.14.0
- Numpy 1.17.1
- Matplotlib 3.1.1
[1] Hu, Jie, et al. (2018). Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.