https://github.com/algocodes/ldp_layer

Local Derivative Pattern as a layer in deep learning models
https://github.com/algocodes/ldp_layer

convolutional-neural-networks custom-layer deep-learning face-recognition feature-extraction localderivativepattern tensorflow

Last synced: 6 months ago
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Local Derivative Pattern as a layer in deep learning models

• Host: GitHub
• URL: https://github.com/algocodes/ldp_layer
• Owner: algocodes
• License: mit
• Created: 2023-10-27T19:56:00.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2023-10-28T20:02:38.000Z (over 2 years ago)
• Last Synced: 2025-09-23T07:55:20.329Z (10 months ago)
• Topics: convolutional-neural-networks, custom-layer, deep-learning, face-recognition, feature-extraction, localderivativepattern, tensorflow
• Language: Python
• Homepage:
• Size: 1.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# A new deep learning layer using local derivative patterns 

This repository contains the Local Derivative Patterns as a Deep Learning layer implemented in the paper. The example on LFW - Labeled Faces in the Wild dataset compares a model with LDP layers and a baseline CNN model. 

LDP layer can be used from the source or can be installed as a package using the project:

            pip install tflocalpattern

[Kaggle] (https://www.kaggle.com/code/devrimakgn/a-dl-model-with-ldp-layers-face-recognition)

## Example evaluation on LFW - Labeled Faces in the Wild dataset: ldp_test.py

###  Training results:

![alt text](images/hist_train.png)

###  Validation results:

![alt text](images/hist_loss.png)

###  Evaluation on test dataset:

  model_ldp - test accuracies for example runs:

              0.9342 0.9473, 0.9429, 0.9298, 0.8991, 0.9078, 0.9342, 0.9254, 0.9122, 0.9166 

  model_base - test accuracies for example runs:

              0.9122, 0.9035, 0.9035, 0.8947, 0.9035, 0.9078, 0.9210, 0.8991, 0.9078, 0.9122

## Example usages:

### Separately:

  x1 = LDP(mode='single', alpha='0')(x1)    

  x2 = LDP(mode='single',alpha='45')(x2)    

  x3 = LDP(mode='single',alpha='90')(x3)    

  x4 = LDP(mode='single',alpha='135')(x4)   

### Mean of LDP 0, LDP 45, LDP 90,and LDP 135:   

  x = LDP(mode='mean')(x)   

### Separate features:   

  x = LDP(mode='multi')(x)    

  

## Processing Cifar10 dataset with LDP:

### Replace Cifar10 with your dataset for processing in the example file cifar_text.py

![alt text](images/cifar10_ldb.png)

##  LDP 0 features:

![alt text](images/ldp_0.png)

##  LDP 45 features:

![alt text](images/ldp_45.png)

##  LDP 90 features:

![alt text](images/ldp_90.png)

##  LDP 135 features:

![alt text](images/ldp_135.png)

Reference study:

[Akgun, Devrim. "TensorFlow based deep learning layer for Local Derivative Patterns." Software Impacts 14 (2022): 100452 https://doi.org/10.1016/j.simpa.2022.100452](https://www.sciencedirect.com/science/article/pii/S2665963822001361)

Similar studies:

https://github.com/dwday/lbp_tensorflow_python

https://github.com/dwday/lbp_pytorch_python-