This project implements a neural network model designed to tackle image classification challenges by leveraging a broad range of natural language annotations. The model's unique architecture allows it to perform multiple classification tasks without explicit optimization for each specific task. The solution is built using AWS platforms such as SageMaker, API Gateway, and S3, enabling scalability and integration with cloud-based services.
In this project, we present a neural network model that is capable of classifying images based on a wide array of natural language annotations. Unlike traditional models, which are optimized for specific tasks, this model generalizes its architecture to support diverse classification tasks without the need for explicit optimization on each. This flexibility makes it well-suited for real-world applications where tasks may evolve or expand over time.
- Multi-task classification: Capable of performing diverse image classification tasks using the same architecture.
- Natural language annotations: Leverages rich natural language metadata to improve the classification process.
- Scalable AWS integration: Built on AWS platforms for easy scalability, including SageMaker for training, S3 for storage, and API Gateway for deployment.
- Neural Networks: Core architecture for performing image classification.
- AWS SageMaker: For training the model efficiently on cloud resources.
- AWS S3: To store training data and model artifacts.
- AWS API Gateway: To serve the model and provide a RESTful API interface for interacting with it.
- Python: Primary programming language used for model development and integration.
Contributions are welcome! If you find any bugs or have ideas for improvements, feel free to:
- Fork the repository
- Create a new branch (
git checkout -b feature-branch) - Commit your changes (
git commit -m 'Add new feature') - Push to the branch (
git push origin feature-branch) - Open a pull request
This project is licensed under the MIT License - see the LICENSE file for details.