Project One — Image Classification using Deep Learning Algorithms

Introduction

In this project, we will demonstrate an end-to-end image classification workflow using deep learning algorithms. We will utilize the CIFAR-10 dataset, which contains 60,000 32x32 color images belonging to 10 different classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 test images. Our goal is to build a deep learning model that can accurately classify images from the CIFAR-10 dataset.

By following the provided file structure and the sample code in this article, you will be able to create a well-organized image classification project, which will make it easier for others to understand and reproduce your work.

Here’s the sample file structure for the image classification project:

image_classification_project/
│
├── README.md                           # Project overview and instructions
│
├── data/                               # Folder containing raw and processed data
│   ├── cifar10/                        # CIFAR-10 data (automatically downloaded)
│
├── notebooks/                          # Jupyter notebooks for analysis and modeling
│   ├── 01_data_preprocessing.ipynb     # Data preprocessing notebook
│   ├── 02_model_building.ipynb         # Model building and training notebook
│   └── 03_model_evaluation.ipynb       # Model evaluation notebook
│
├── src/                                # Source code for custom modules and functions
│   ├── __init__.py…