CNN-based deep learning image classifier AI webapp

The Deep Learning Image Classifier AI Webapp is a web application that uses a deep learning model to classify images into various categories. This project leverages a ResNet50 neural network, pre-trained on the ImageNet dataset, to accurately identify and classify images. The system recognises the object in an image uploaded by the user.

copyright @navidbinahmed 2023

Table of Contents

• Project Overview
• Tools and Tech Used
• Features
• Steps for Implementation
• Installation
• Usage
• Demo
• How to Contribute
• License
• Contact

Tools and Tech Used

Key Tools

1. Jupyter Notebook & Anaconda
2. VS Code
3. Docker/WSL 2
4. GitHub/Git CLI
5. Python v3.9
6. Flask
7. HTML/CSS

Key Technologies

• TensorFlow: For deep learning model creation and training.
• Keras: High-level neural networks API.

Features

• Image Upload: Users can upload images for classification.
• Real-time Predictions: Get instant predictions on uploaded images.
• Visual Feedback: Display of the classified category along with confidence scores.
• User-Friendly Interface: Simple and intuitive UI.

Steps for Implementation

1. Created a new conda environment (imageclassifier)

2. Downloaded dataset

3. Model Building i. Preprocessing data ii. EDA/ feature engineering iii. Model building iv. Saved model as a .h5 file

4. Customized app building using Flask

5. Install Docker Desktop with supporting tools (WSL 2)

6. Pushed the repo to Git CLI (and Docker) i. Configuaration commands:

   ```
   git config --global user.name (Navid Bin Ahmed)
   ```
   ```
   git config --global user.email (******)
   ```
   

   ii. used commands:

     ```
     git add <file name>
     
     git add . (adds all files)
     ```
     ```
     git commit -m "message"
     ```
     ```
     git push origin <branch name> master or main
     ```
     ```
     git status
     ```
   

Installation

To get a local copy up and running, follow these simple steps.

Prerequisites

• Python 3.x
• Virtual environment (recommended)

Installation Steps

1. Clone the Repository

   git clone https://github.com/NavidBinAhmed/Webapp_Deep_Learning_Image_Classifier.git
   cd Webapp_Deep_Learning_Image_Classifier

2. Create and Activate a Virtual Environment

   python -m venv venv
   source venv/bin/activate # On Windows use `venv\Scripts\activate`

3. Install Required Packages

   pip install -r requirements.txt

4. Run the Application

   python app.py

Usage

1. Navigate to http://127.0.0.1:5000/ in your web browser.
2. Upload an image using the provided interface.
3. Click the "Predict" button to get the classification result.
4. The application will display the predicted category along with the confidence score.

Demo

Link

Check out the live demo of the application here.

Expected Output

1. image 1

2. Image 2

   Application in action (to be updated):

How to Contribute

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

• Email: [email protected]