Pull Request for DL-Simplified 💡

Issue Title : #776

Info about the related issue (Aim of the project) : This project is to develop an advanced image classification system that can accurately identify the facial expressions of pets using cutting-edge deep learning techniques.
Name: Khushi Kalra
GitHub ID: https://www.github.com/abckhush
Email ID: kalra.khushi@outlook.com
Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) GSSoC'24 Contributor

Closes: #776

Describe the add-ons or changes you've made 📃

This project focuses on developing a sophisticated image classification system to identify different facial expressions in pets using deep learning techniques. The system leverages three state-of-the-art convolutional neural network (CNN) architectures: EfficientNet and ResNet50, along with Vision Transformer (ViT) architecture, each known for their robustness and high performance in image recognition tasks.

Type of change ☑️

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

How Has This Been Tested? ⚙️

The models' performance was evaluated using the following metrics:

Accuracy
F1 Score
Confusion Matrix
Cross-Validation

Checklist: ☑️

[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.
[X] My changes generate no new warnings.
[X] I have added things that prove my fix is effective or that my feature works.
[X] Any dependent changes have been merged and published in downstream modules.

abhisheks008 / DL-Simplified

Pet's Facial Expression Detection #806