TensorFlow implementation of "Aggregated Residual Transformations for Deep Neural Networks"
TensorFlow implementation of “Aggregated Residual Transformations for Deep Neural Networks”
ResNet-TF2
WideResNet(WRN)-TF2
ResNet-with-LRWarmUp-TF2
ResNet-with-SGDR-TF2
The three ways for construct ResNeXt block [1].
Indicator | Value |
---|---|
Accuracy | 0.99370 |
Precision | 0.99371 |
Recall | 0.99364 |
F1-Score | 0.99367 |
Confusion Matrix
[[ 977 0 1 0 0 0 0 0 2 0]
[ 0 1129 2 0 0 0 1 2 1 0]
[ 0 1 1026 0 1 0 0 3 1 0]
[ 0 0 2 1007 0 1 0 0 0 0]
[ 0 0 0 0 976 0 1 0 0 5]
[ 1 0 0 4 0 883 2 0 0 2]
[ 1 1 0 0 1 1 953 0 1 0]
[ 0 1 2 0 0 0 0 1024 1 0]
[ 2 0 2 1 0 0 0 2 965 2]
[ 0 1 0 0 4 2 0 3 2 997]]
Class-0 | Precision: 0.99592, Recall: 0.99694, F1-Score: 0.99643
Class-1 | Precision: 0.99647, Recall: 0.99471, F1-Score: 0.99559
Class-2 | Precision: 0.99130, Recall: 0.99419, F1-Score: 0.99274
Class-3 | Precision: 0.99506, Recall: 0.99703, F1-Score: 0.99604
Class-4 | Precision: 0.99389, Recall: 0.99389, F1-Score: 0.99389
Class-5 | Precision: 0.99549, Recall: 0.98991, F1-Score: 0.99269
Class-6 | Precision: 0.99582, Recall: 0.99478, F1-Score: 0.99530
Class-7 | Precision: 0.99033, Recall: 0.99611, F1-Score: 0.99321
Class-8 | Precision: 0.99178, Recall: 0.99076, F1-Score: 0.99127
Class-9 | Precision: 0.99105, Recall: 0.98811, F1-Score: 0.98958
Total | Accuracy: 0.99370, Precision: 0.99371, Recall: 0.99364, F1-Score: 0.99367
[1] Saining Xi et al. (2016). Aggregated Residual Transformations for Deep Neural Networks. arXiv preprint arXiv:1611.05431.