ResNet-TF2
ResNeXt-TF2
WideResNet(WRN)-TF2
ResNet-with-SGDR-TF2
Learning Rate Warm-Up strategy [1].
Accuracy, Loss, and Learning Rate graph in training procedure.
| Indicator | Value |
|---|---|
| Accuracy | 0.98570 |
| Precision | 0.98572 |
| Recall | 0.98561 |
| F1-Score | 0.98564 |
Confusion Matrix
[[ 972 0 1 0 0 0 3 2 2 0]
[ 0 1123 3 2 3 1 0 0 3 0]
[ 2 2 1021 1 1 0 0 1 3 1]
[ 0 0 0 1003 0 3 0 1 3 0]
[ 0 0 1 0 973 0 0 0 2 6]
[ 2 0 0 7 0 873 2 0 4 4]
[ 4 1 0 1 1 2 943 0 6 0]
[ 0 4 6 4 1 0 0 1006 2 5]
[ 3 0 3 2 0 0 0 1 962 3]
[ 3 2 0 5 7 2 0 3 6 981]]
Class-0 | Precision: 0.98580, Recall: 0.99184, F1-Score: 0.98881
Class-1 | Precision: 0.99205, Recall: 0.98943, F1-Score: 0.99074
Class-2 | Precision: 0.98647, Recall: 0.98934, F1-Score: 0.98791
Class-3 | Precision: 0.97854, Recall: 0.99307, F1-Score: 0.98575
Class-4 | Precision: 0.98682, Recall: 0.99084, F1-Score: 0.98882
Class-5 | Precision: 0.99092, Recall: 0.97870, F1-Score: 0.98477
Class-6 | Precision: 0.99473, Recall: 0.98434, F1-Score: 0.98951
Class-7 | Precision: 0.99211, Recall: 0.97860, F1-Score: 0.98531
Class-8 | Precision: 0.96878, Recall: 0.98768, F1-Score: 0.97814
Class-9 | Precision: 0.98100, Recall: 0.97225, F1-Score: 0.97661
Total | Accuracy: 0.98570, Precision: 0.98572, Recall: 0.98561, F1-Score: 0.98564
- Python 3.7.6
- Tensorflow 2.1.0
- Numpy 1.18.1
- Matplotlib 3.1.3
[1] Priya Goyal et al. (2017). Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. arXiv preprint arXiv:1706.02677.