[Project Addition]: Thirty Musical Instruments Image Classification

[aaradhyasinghgaur]

Deep Learning Simplified Repository (Proposing new issue)

🔴 Project Title : 30 Musical Instruments Image Classification

🔴 Aim : To classify 30 different types of music instruments for example accordian , bagpipes and banjo etc. using custom and pre-trained dl models

🔴 Dataset : https://www.kaggle.com/datasets/gpiosenka/musical-instruments-image-classification/data

🔴 Approach : Try to use 3-4 algorithms to implement the models and compare all the algorithms to find out the best fitted algorithm for the model by checking the accuracy scores. Also do not forget to do a exploratory data analysis before creating any model.

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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 : Aaradhya Singh
• GitHub Profile Link : https://github.com/kyra-09
• Email ID : [email protected]
• Participant ID (if applicable):
• Approach for this Project :
  1. Develop custom deep learning models and utilize pre-trained models like VGG16 and ResNet50 for classifying 30 different music instruments.
  2. Apply data augmentation techniques to enhance model generalization and robustness.
  3. Train and evaluate models using metrics such as accuracy score, confusion matrix, and plot graphs to visualize training progress.
  4. Compare and select the best-performing model between custom and pre-trained models based on evaluation results.
  5. Document the entire process and provide a README file with detailed descriptions and conclusions for future enhancements.
• What is your participant role? (Mention the Open Source program) GSSOC-2024 Contributor

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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! 😊

[abhisheks008]

Assigned @kyra-09

[github-actions]

Hello @kyra-09! Your issue #697 has been closed. Thank you for your contribution!