IntelliSort is a project aimed at classifying images into various categories such as 'docs', 'handwritten_docs', 'People', 'pets', and 'signatures' using machine learning models.
In today's digital age, managing and organizing vast amounts of data efficiently is crucial. The ability to automatically classify images into specific categories can significantly streamline workflows, especially in scenarios involving document management, identification, and personalization. IntelliSort addresses this need by providing a robust solution for image classification.
- Multi-class Classification: IntelliSort can classify images into multiple predefined categories, enabling versatile usage scenarios.
- Scalability: The underlying machine learning models are designed to handle large datasets efficiently, ensuring scalability as the project grows.
- Accuracy: Through continuous refinement and optimization, IntelliSort aims to achieve high accuracy in image classification tasks.
- Easy Integration: The project is built with ease of integration in mind, allowing developers to seamlessly incorporate image classification capabilities into their applications.
To use IntelliSort for image classification, follow these steps:
- Installation: Install the required dependencies by running
pip install -r requirements.txt. - Model Training: Train the image classification model using your dataset or use the pre-trained model provided.
- Classification: Use the trained model to classify images into the predefined categories.
To get started with IntelliSort, clone the repository and refer to the documentation for detailed instructions:
git clone https://github.com/RushiChaganti/IntelliSort.git
cd IntelliSort