Description

Dive into the power of AI with this project that leverages the ImageAI library for image classification. Utilizing a pre-trained MobileNetV2 model, this project demonstrates how to accurately classify images and predict objects.

Experience the power of AI with this state-of-the-art image classification project. Whether you're a developer, researcher, or AI enthusiast, this project will provide a solid foundation for implementing advanced image analysis in your applications. Happy coding!

Libraries Used

• ImageAI
• OS

Key Features

• Load and preprocess images for classification.
• Utilize the MobileNetV2 model for accurate predictions.
• Display classification results with probabilities.

Use Case

Ideal for developers and researchers looking to implement image classification in their projects, such as automated image tagging, object detection, or any application requiring image analysis.

Problems and Solutions

Problems

• Manual Image Tagging and Classification: Tagging and classifying images manually is time-consuming and can lead to inconsistencies.

• Real-Time Image Recognition: Real-time applications, such as surveillance systems, require fast and accurate image recognition, which is challenging without automation.

• Accessible AI Tools: Advanced AI tools for image classification are often not accessible to small businesses or individual developers.

Solutions

• Automated Image Tagging and Classification: The AI model can automatically classify and tag images accurately, saving time and ensuring consistency.

• Real-Time Image Recognition: The pre-trained MobileNetV2 model provides fast and accurate image recognition, making it suitable for real-time applications.

• Accessible AI Tools: This project provides an easy-to-use tool for image classification, making advanced AI technology accessible to a wider audience.

Installation

Follow these steps to set up the project:

# Clone the repository
git clone https://github.com/Ableboy/Image-Prediction.git

# Navigate into the project directory
cd Image-Prediction

# Create a virtual environment (optional but recommended)
python -m venv venv

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

# Install dependencies
pip install imageai

# Additionally, download the MobileNetV2 model and place it in the project directory

Usage

• Ensure you have the necessary model file (mobilenet_v2-b0353104.pth) in the project directory.
• Replace the image path in the code with the path to your image file.
• Run the script to see the classification results.

Contributing

Contributions to this image classification project are welcome! To contribute, please follow these steps:

1. Fork the Repository: Click the "Fork" button at the top right of this repository's page on GitHub.

2. Clone Your Fork: Clone your forked repository to your local machine using:

git clone https://github.com/Ableboy/Image-Prediction.git

3. Create a Branch: Create a new branch for your feature or bug fix:

git checkout -b feature-name

4. Make Your Changes: Make your changes in the code.

5. Commit Your Changes: Commit your changes with a clear message:

git commit -m 'Add new feature'

6. Push to Your Fork: Push your changes to your forked repository:

git push origin feature-name

7. Create a Pull Request: Open a pull request on the original repository, describing your changes.

License

This project is licensed under the MIT License.