imageClassifier

Here is a structured README file for your image classifier project, integrating the given information:

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Image Classifier Project

This project is part of the AI Programming with Python Nanodegree Program, where you will develop an image classifier capable of recognizing different species of flowers. You will build and train a deep learning model and convert it into a command-line application for real-world use.

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Table of Contents

1. Introduction
2. Dataset Overview
3. Project Workflow
4. Installation
5. Usage
6. Future Applications
7. License

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Introduction

As AI algorithms are increasingly integrated into everyday applications, this project demonstrates how to develop an image classifier to identify flower species. For instance, this could be used in a smartphone app to identify flowers captured by the camera.

By completing this project, you will:

• Train a deep learning model to classify flower images.
• Build a command-line application that predicts flower species based on user input.
• Learn skills to develop similar models for other datasets and applications.

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Dataset Overview

The dataset used in this project is from Oxford's 102 Flower Categories. It contains 102 flower species with labeled images. Below are some example images from the dataset:

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Project Workflow

The project is divided into the following steps:

1. Loading and Preprocessing Data
   Prepare the image dataset by resizing, normalizing, and augmenting images for training.

2. Model Training
   Build and train a deep learning model using a pre-trained network (e.g., VGG16, ResNet).

3. Prediction and Evaluation
   Test the model on unseen images and evaluate its accuracy. Implement a command-line interface for predictions.

4. Deployment
   Export the trained model and use it for real-world applications.

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Installation

To run the project, ensure you have the following installed:

• Python 3.x
• PyTorch
• NumPy
• Matplotlib
• torchvision
• jupyter notebook

Clone this repository:

git clone https://github.com/udacity/aipnd-project.git
cd aipnd-project

Install dependencies:

pip install -r requirements.txt

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Usage

Train the Model

Run the training script:

python train.py --data_dir path_to_dataset --save_dir path_to_save_model --epochs 10

Make Predictions

Use the prediction script:

python predict.py --image_path path_to_image --checkpoint path_to_model

Options

• --top_k: Number of top predictions to display.
• --category_names: Path to JSON file mapping categories to names.

Example:

python predict.py --image_path flower.jpg --checkpoint checkpoint.pth --top_k 5 --category_names cat_to_name.json

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Unleash your creativity by building a custom dataset and creating unique applications!

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License

This project is licensed under the MIT License. See the LICENSE file for details.