FEAT:Butterfly Image Classification using Deep Learning

[IkkiOcean]

Closes: #78

• AIM: The primary goal of this project is to build and compare various deep learning models to accurately classify butterfly images into their respective species.

• Approach Summary for Butterfly Image Classification

  1. Dataset Preprocessing: Images were resized, normalized, and split into training, validation, and test sets. Data augmentation was applied to improve generalization.
  2. Models Implemented:
     • CNN: Custom convolutional layers for image classification (87.36% accuracy).
     • ResNet50: Fine-tuned pre-trained model using transfer learning (80.82% accuracy).
     • EfficientNet: Fine-tuned for optimal depth and efficiency, achieving the best performance (91.85% accuracy).
  3. Conclusion: EfficientNet was the best-performing model, making it ideal for butterfly species classification.

  Program : GSSOC-ext and hacktoberfest

[github-actions]

Thank you for submitting your pull request! 🙌 We'll review it as soon as possible. In the meantime, please ensure that your changes align with our CONTRIBUTING.md. If there are any specific instructions or feedback regarding your PR, we'll provide them here. Thanks again for your contribution! 😊

[IkkiOcean]

Can you make it level 2 or 3 at least. I have implemented multiple Deep Learning models and evaluated them