Pull Request for DL-Simplified 💡

Issue Title : Thirty Musical Instruments Image Classification

Info about the related issue (Aim of the project) : The main purpose of this project is to classify 30 different musical instruments from the dataset (mentioned below) using various image detection/recognition models and comparing their accuracy.
Name: Aaradhya Singh
GitHub ID: https://github.com/kyra-09
Email ID: aaradhyasinghgaur@gmail.com
Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSOC-2024 Contributor

Closes: #697

Develop custom deep learning models and utilize pre-trained models like VGG16 and ResNet50 for classifying 30 different music instruments.
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.
Compare and select the best-performing model between custom and pre-trained models based on evaluation results.
Document the entire process and provide a README file with detailed descriptions and conclusions for future enhancements.

What sort of change have you made:

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Code style update (formatting, local variables)
[ ] Breaking change (fix or feature that would cause existing functionality to not work as expected)
[ ] This change requires a documentation update

Describe how it has been tested Describe how have you verified the changes made

[x] My code follows the guidelines of this project.
[x] I have performed a self-review of my own code.
[x] I have commented my code, particularly wherever it was hard to understand.
[x] I have made corresponding changes to the documentation.
[ ] My changes generate no new warnings.
[x] I have added things that prove my fix is effective or that my feature works.
[ ] Any dependent changes have been merged and published in downstream modules.