Deep Learning Image Classification using CNN, AlexNet and VGG16

Project Overview

This project was completed as part of my deep learning internship. The main objective was to understand image classification using different deep learning models and compare their performance on benchmark datasets.

The models implemented in this project are:

• Convolutional Neural Network (CNN)
• AlexNet
• VGG16

The models were trained and tested on different datasets to study how their performance changes with increasing dataset complexity.

Datasets Used

• MNIST
• Fashion-MNIST
• CIFAR-10
• CIFAR-100

Tools and Libraries

• Python
• TensorFlow
• Keras
• NumPy
• Matplotlib
• Scikit-learn
• Google Colab

Model Performance

Model	Dataset	Accuracy
CNN	CIFAR-100	56.26%
VGG16	CIFAR-100	52.46%
AlexNet	CIFAR-100	50.26%

Model Comparison

The following figure compares the final accuracy of all three models on the CIFAR-100 dataset.

Project Structure

Deep-Learning-Image-Classification
│
├── notebooks
├── models
├── images
├── requirements.txt
└── README.md

Key Learnings

Through this project, I learned:

• Image preprocessing
• Convolutional Neural Networks
• AlexNet architecture
• VGG16 architecture
• Batch Normalization
• Dropout
• Data Augmentation
• Hyperparameter tuning
• Model evaluation and comparison

Future Scope

In the future, I plan to work with more advanced deep learning models such as ResNet and EfficientNet and further improve the classification accuracy.

Author

Somayajula Sree Shashank

B.Tech, Computer Science and Engineering (Data Science)

GITAM University