Image-Classification-using-Convolutional-Neural-Networks

[UTSAVS26]

Pull Request for DL-Simplified 💡

Issue #730

Issue Title: Image Classification using Convolutional Neural Networks

Info about the related issue (Aim of the project): The goal of this project is to implement and compare the performance of various deep learning models, including LeNet-5, MobileNet, ResNet50, Simple CNN, and VGG16, for image classification tasks. By training these models on datasets like CIFAR-10 and MNIST, the project aims to evaluate and analyze their respective accuracies, loss values, and generalization capabilities. This comparative study will highlight the strengths and weaknesses of each architecture and provide insights into their effectiveness for practical image classification applications. The findings will guide the selection of the most suitable model architecture for robust image classification.

Name: Utsav Singhal GitHub ID: UTSAVS26 Email ID: utsavsinghal26@gmail.com Identify yourself: SSoC 2024 participant
Closes: #730

Describe the add-ons or changes you've made 📃
This project involves the implementation and comparison of five deep learning models: LeNet-5, MobileNet, ResNet50, Simple CNN, and VGG16. Each model is designed for image classification tasks and has been tested on datasets like CIFAR-10 and MNIST. The models were trained with data augmentation to improve generalization and their performances were evaluated based on metrics such as accuracy, precision, recall, and F1-score. The project aims to identify which model best balances accuracy and generalization, offering insights into the advantages of different architectural features like residual connections and depthwise separable convolutions in deep learning.

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 project involved evaluating five deep learning models—LeNet-5, MobileNet, ResNet50, Simple CNN, and VGG16—for image classification tasks. The models were trained on the CIFAR-10 and MNIST datasets, with the data split into training, validation, and test sets. Training data underwent extensive augmentation to enhance model generalization. Each model was trained using categorical cross-entropy loss and the Adam optimizer, with callbacks for early stopping, best model checkpointing, and learning rate reduction. Performance was assessed on test sets using accuracy, precision, recall, F1-score, and confusion matrices. Results were visualized through accuracy curves and detailed classification reports. This comprehensive evaluation aimed to identify the most effective architecture for robust image classification.

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.

[github-actions[bot]]

Our team will soon review your PR. Thanks @UTSAVS26 :)

[UTSAVS26]

Sorry for the trouble, please review the changes made now https://github.com/abhisheks008/DL-Simplified/pull/815