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Convolutional Neural Networks on Image Classification using a genetic algorithm for optimization

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Bird_Species_Classification

This is a project developed for the curricular unit of Soft Computing.

Problem Statment

Classify the bird species that appear on an image based on the use of a Convolutional Neural Network model (CNN).

Learning Objectives

  • Preparation and analysis of dataset;
  • Training and validation of learning models, specifically Convolutional Neural Networks (CNN);
  • Use of Genetic Algorithms (GA) for learning model hyper parameter optimization, structure optimization and loss function optimization.

Dataset

The proposed dataset has the following features:

  • Bird Species: 250
  • Training Images: 35215 (not balanced, however has at least 100 training image files per species);
  • Validation Images: 1250 (5 per species);
  • Test Images: 1250 (5 per species);
  • Images Size: 224 x 224 x 3 color channels in jpg format;
  • Species gender: 80% of total images are of male while the remaining 20% are of female - the classifier may not perform as well on female specie images.

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Convolutional Neural Networks on Image Classification using a genetic algorithm for optimization

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