GitHub Repository for Image Classification using InceptionV3

This GitHub repository contains a Jupyter Notebook (image_classification_inceptionv3.ipynb) that demonstrates how to perform image classification using the InceptionV3 model and the TensorFlow/Keras framework. The notebook guides you through the process of training a deep learning model to classify images into one of the predefined classes. Below, you will find an explanation of the key components and steps in the notebook.

Table of Contents

1. Introduction
2. Getting Started
3. Data Preparation
4. Model Architecture
5. Model Training
6. Model Evaluation
7. Results and Analysis

Introduction

In this notebook, we use the InceptionV3 pre-trained model for image classification. We walk you through the process of fine-tuning this model on your own dataset. The notebook includes code for data preprocessing, model creation, training, and evaluation.

Getting Started

This section contains all the necessary imports and installations for the libraries used in the notebook. Make sure to have TensorFlow and other required packages installed before running the notebook.

Data Preparation

In this section, we load and preprocess the dataset for training. The dataset should be organized into folders, where each folder corresponds to a class. The getFeaturesArray function is used to load and preprocess the images, and it returns the data and labels.

Model Architecture

Here, we define the model architecture using InceptionV3 as a base model. We add custom layers on top of the pre-trained model to adapt it for our specific image classification task. The model is compiled with suitable parameters.

Model Training

This section covers the training process of the model. It includes splitting the data into training and testing sets, and then training the model using the fit method. Various training parameters such as learning rate, batch size, and number of epochs are defined.

Model Evaluation

After training, the model's performance is evaluated using the test set. You'll find code for evaluating accuracy and plotting training and validation loss and accuracy over epochs.

Results and Analysis

The notebook concludes with an analysis of the results. It provides classification reports and other evaluation metrics to assess the model's performance on your dataset.

Please follow the notebook (image_classification_inceptionv3.ipynb) for a detailed step-by-step explanation of the code and a hands-on guide to image classification using InceptionV3. Enjoy experimenting with your own image classification task!