https://github.com/yeonghyeon/sknet-tf2

TensorFlow implementation of "Selective Kernel Networks"
https://github.com/yeonghyeon/sknet-tf2

convolutional-neural-network convolutional-neural-networks deep-learning deep-neural-networks mnist mnist-classification mnist-dataset selective-kernel-networks

Last synced: about 2 months ago
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TensorFlow implementation of "Selective Kernel Networks"

• Host: GitHub
• URL: https://github.com/yeonghyeon/sknet-tf2
• Owner: YeongHyeon
• License: mit
• Created: 2020-02-20T04:23:46.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-21T06:23:03.000Z (almost 5 years ago)
• Last Synced: 2023-04-30T10:40:46.047Z (over 1 year ago)
• Topics: convolutional-neural-network, convolutional-neural-networks, deep-learning, deep-neural-networks, mnist, mnist-classification, mnist-dataset, selective-kernel-networks
• Language: Python
• Homepage:
• Size: 127 KB
• Stars: 11
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
[TensorFlow 2] Selective Kernel Networks

=====

## Concept



    

  
The concept of SKNet [1].



## Performance

|Indicator|Value|

|:---|:---:|

|Accuracy|0.99160|

|Precision|0.99173|

|Recall|0.99154|

|F1-Score|0.99161|

```

Confusion Matrix

[[ 977    0    0    0    0    0    2    0    0    1]

 [   0 1133    1    1    0    0    0    0    0    0]

 [   1    1 1024    0    0    0    1    4    1    0]

 [   0    0    1 1000    0    5    0    1    0    3]

 [   0    0    2    0  964    0    1    2    1   12]

 [   1    0    0    4    0  885    1    0    0    1]

 [   1    3    0    0    0    1  953    0    0    0]

 [   0    7    6    0    2    0    0 1009    0    4]

 [   1    0    1    1    0    1    0    0  965    5]

 [   0    0    0    0    3    0    0    0    0 1006]]

Class-0 | Precision: 0.99592, Recall: 0.99694, F1-Score: 0.99643

Class-1 | Precision: 0.99038, Recall: 0.99824, F1-Score: 0.99430

Class-2 | Precision: 0.98937, Recall: 0.99225, F1-Score: 0.99081

Class-3 | Precision: 0.99404, Recall: 0.99010, F1-Score: 0.99206

Class-4 | Precision: 0.99484, Recall: 0.98167, F1-Score: 0.98821

Class-5 | Precision: 0.99215, Recall: 0.99215, F1-Score: 0.99215

Class-6 | Precision: 0.99478, Recall: 0.99478, F1-Score: 0.99478

Class-7 | Precision: 0.99311, Recall: 0.98152, F1-Score: 0.98728

Class-8 | Precision: 0.99793, Recall: 0.99076, F1-Score: 0.99433

Class-9 | Precision: 0.97481, Recall: 0.99703, F1-Score: 0.98579

Total | Accuracy: 0.99160, Precision: 0.99173, Recall: 0.99154, F1-Score: 0.99161

```

## Requirements

* Python 3.7.6  

* Tensorflow 2.1.0  

* Numpy 1.18.1  

* Matplotlib 3.1.3  

## Reference

[1] Xiang Li et al. (2019). Selective Kernel Networks. arXiv preprint arXiv:1903.06586.