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Optimizing VGG16 deep learning model with enhanced hunger games search for logo classification.

Mohammed Hussain1, Thaer Thaher2, Mohamed Basel Almourad1

  • 1College of Technological Innovation, Zayed University, Dubai, United Arab Emirates.

Scientific Reports
|December 31, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced Hunger Games Search (EHGS) algorithm to optimize VGG16 hyperparameters for logo classification. The EHGS-VGG16 model achieved superior accuracy, demonstrating the power of evolutionary optimization in deep learning for image recognition tasks.

Keywords:
Computer visionConvolution neural networkHunger games searchHyperparametersLogo classificationMetaheuristics

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Area of Science:

  • Computer Vision and Machine Learning
  • Swarm Intelligence and Optimization Algorithms
  • Deep Learning Architectures for Image Recognition

Background:

  • Accurate logo classification is crucial but challenging due to variations in size, orientation, and background complexity.
  • Deep learning models like VGG16 show promise but require extensive hyperparameter tuning.
  • Existing swarm intelligence algorithms, including the Hunger Games Search (HGS), face limitations like restricted population diversity and local optima.

Purpose of the Study:

  • To propose an optimized deep learning architecture, EHGS-VGG16, by enhancing the Hunger Games Search (HGS) algorithm for hyperparameter tuning.
  • To improve the exploration capability of HGS through modified search strategies, including 'local best' and 'local escaping mechanisms'.
  • To evaluate the effectiveness of the proposed EHGS-VGG16 model for logo classification.

Main Methods:

  • Developed an enhanced Hunger Games Search (EHGS) algorithm with improved exploration capabilities.
  • Evaluated the EHGS algorithm on 30 real-valued benchmark functions from the IEEE CEC2014 suite.
  • Implemented and tested a VGG16 model on the Flickr-27 logo classification dataset, comparing it with other state-of-the-art deep learning models. Integrated EHGS for hyperparameter optimization.

Main Results:

  • The VGG16 model achieved high performance, outperforming ResNet50V2, InceptionV3, DenseNet121, EfficientNetB0, and MobileNetV2 on the Flickr-27 dataset.
  • The proposed EHGS algorithm demonstrated improved performance in benchmark function evaluations.
  • Integrating EHGS with VGG16 for hyperparameter tuning resulted in a further 3% improvement in logo classification accuracy.

Conclusions:

  • The EHGS-VGG16 model significantly enhances logo classification accuracy compared to standard VGG16 and other deep learning models.
  • The enhanced Hunger Games Search algorithm effectively addresses limitations of the standard HGS, improving exploration and avoiding local optima.
  • Combining evolutionary optimization techniques with deep learning offers a promising approach for improving accuracy in complex image recognition tasks like logo classification.