Food Detection Gradle Project

GitHub Repository: Food Detection Gradle Project

Description:
The Food Detection Gradle Project is a Java-based application that identifies and classifies food items using machine learning models. Built with a focus on modularity and scalability, this project integrates Gradle as the build automation tool and employs image recognition techniques to detect various food items. It showcases the combination of Java and AI in real-world applications.

Key Features:

• Food Item Detection: Recognizes and classifies food items from images using trained models.

• Scalable Build System: Utilizes Gradle for dependency management and project organization.

• Seamless Integration: Designed for easy integration with existing Java projects.

• Customizable ML Model: Supports the use of custom-trained models for unique datasets.

• Cross-Platform Support: Runs efficiently on different platforms with Java runtime support.

Technologies Used:

• Programming Language: Java

• Build Tool: Gradle

• Libraries: OpenCV, TensorFlow (via Java bindings), Apache Commons

• Machine Learning Model: Pre-trained TensorFlow model or a user-provided custom model

Challenges Solved:

• Integrated machine learning capabilities into a Java-based environment.

• Managed dependencies and streamlined builds with Gradle for faster development.

• Achieved efficient image preprocessing and classification within Java's ecosystem.

Installation Steps

1. Clone the Repository:

   git clone https://github.com/fanik041/Food_Detection_Gradle_Projecct.git cd Food_Detection_Gradle_Projecct

2. Set Up Gradle:
   Ensure you have Gradle installed. If not, download and install Gradle from Gradle's official website. Verify the installation with:

   gradle -v

3. Download Required Dependencies:
   Gradle automatically handles dependencies. Sync the project to download them:

   gradle build

4. Configure the Dataset:
   Place your image dataset or test images in the designated folder (e.g., resources/images/). Update the configuration file or script with the dataset path if required.

5. Run the Project:
   Use the Gradle command to execute the main application:

   gradle run

6. Customize the ML Model (Optional):
   Replace the default TensorFlow model file in the resources/model/ directory with your custom model. Ensure the model's input and output structure matches the application logic.

7. Test the Application:
   Run tests to ensure the application functions as expected:

   gradle test

Note: Ensure you have Java JDK (version 8 or higher) installed on your system and properly configured in your environment variables.