Hair Type Classification using Deep Learning v2

[abhisheks008]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : Hair Type Classification using Deep Learning
🔴 Aim : To build an model for classifying various hair types
🔴 Dataset : https://www.kaggle.com/datasets/kavyasreeb/hair-type-dataset/data
🔴 Approach : To enhance the classification accuracy, I propose building an ensemble model by combining predictions from:

• VGG16
• ResNet50
• EfficientNet B4

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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🔴🟡 Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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✅ To be Mentioned while taking the issue :

• Full name :
• GitHub Profile Link :
• Email ID :
• Participant ID (if applicable):
• Approach for this Project :
• What is your participant role? WOC 2025

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions]

Thank you for creating this issue! We'll look into it as soon as possible. Your contributions are highly appreciated! 😊

[Rangan2005]

For this issue my approach involves implementing three models: ResNet50, EfficientNetB4 and Vision Transformers.
I will implement ResNet50 and EfficientNetB4 using keras and Vision Transformers using Hugging Face Transformers library.
After Training the models, I'd evaluate their performance using metrics like accuracy, precision, recall and F1 scores.

[abhisheks008]

Assigned @Rangan2005