Image Classification using Pretrained Models (VGG16, ResNet50)

The course is a comprehensive program designed for individuals interested in advancing their skills in computer vision and deep learning. This hands-on course focuses on image classification using two powerful pretrained models, VGG16 and ResNet50, and applies them to the challenging CIFAR-10 dataset. Participants will learn the entire pipeline of building, fine-tuning, and evaluating deep learning models for multi-class image classification tasks. By the end of the course, students will have the knowledge and practical experience to tackle complex image classification projects.

Learning Outcomes:

Upon completing this course, participants will:

- Develop a strong understanding of image classification and the CIFAR-10 dataset.

- Master Python programming for deep learning applications.

- Learn to use state-of-the-art pretrained models like VGG16 and ResNet50.

- Acquire expertise in data preprocessing techniques for image datasets.

- Build, fine-tune, and evaluate deep learning models for multi-class image classification.

- Apply transfer learning principles to leverage pretrained models.

- Gain proficiency in using TensorFlow or PyTorch for deep learning tasks.

- Evaluate model performance using appropriate metrics and visualization tools.

- Be prepared to work on diverse computer vision projects beyond the course scope.

Prerequisites:

- Proficiency in Python programming.

- A basic understanding of machine learning and deep learning concepts.

- Access to a Python development environment with libraries such as TensorFlow or PyTorch for deep learning, NumPy for data manipulation, and Matplotlib for data visualization.

Libraries and Programming Language Used:

- Python for coding and scripting.

- TensorFlow or PyTorch for building and training deep learning models.

- NumPy for data manipulation.

- Matplotlib for data visualization.

Course Syllabus:

Introduction to Image Classification and CIFAR-10 Dataset

   - Overview of image classification tasks.

   - Introduction to the CIFAR-10 dataset.

Setting Up the Development Environment

   - Installing Python and required libraries.

   - Preparing the development environment for deep learning projects.

Understanding Pretrained Models

   - Introduction to VGG16 and ResNet50 architectures.

   - Leveraging pretrained models for image classification.

CIFAR-10 Dataset Exploration

   - Data loading and initial exploration.

   - Visualizing sample images from CIFAR-10.

Data Preprocessing

   - Data loading, preprocessing, and augmentation.

   - Preparing data for model training.

Building and Fine-Tuning Models

   - Designing and configuring CNN architectures with VGG16 and ResNet50.

   - Fine-tuning models for the CIFAR-10 dataset.

Training Deep Learning Models

   - Setting up training parameters.

   - Monitoring model training and preventing overfitting.

Model Evaluation and Metrics

   - Evaluating model performance using accuracy, confusion matrices, and more.

   - Visualizing model predictions and errors.

   - Making predictions

Embark on an in-depth journey into computer vision and deep learning with our specialized course. Designed for those who wish to enhance their skills, this hands-on program concentrates on multi-class image classification, leveraging the advanced pretrained models VGG16 and ResNet50 applied to the CIFAR-10 dataset. Participants will delve into the complete process of building, fine-tuning, and evaluating deep learning models for image classification tasks. By the end of this course, students will be equipped with both the knowledge and practical experience necessary to undertake complex image classification projects.