Image Search Engine - Model Training Microservice

Welcome to the Model Training Microservice of our Image Search Engine project! This microservice focuses on training deep learning models for various tasks related to image processing and analysis.

Summary

In this microservice, we handle the training of deep learning models using GPU resources provided by Paper Space. The trained models are crucial components of our Image Search Engine, as they enable us to perform tasks such as image classification, object detection, and feature extraction.

Components

1. Data Preparation

Before training a model, it's essential to have a well-prepared dataset. This involves collecting, preprocessing, and augmenting images to ensure the model's robustness and generalization. We utilize Amazon S3 buckets to store and manage our image datasets efficiently.

2. Model Training

We employ state-of-the-art deep learning frameworks like TensorFlow or PyTorch to train our models. Leveraging GPU resources from Paper Space ensures faster training times and enables us to experiment with complex architectures and large datasets effectively.

3. Model Frontend

After successful training and evaluation, the trained models are deployed within our Image Search Engine architecture. They are integrated with other microservices to perform tasks like image tagging, similarity scoring, and search indexing, ultimately enhancing the overall search experience.

Getting Started

To get started with using this microservice for model training, follow these steps:

1. Setup AWS S3 Bucket: Create an Amazon S3 bucket to store your image datasets. Ensure proper access control and permissions are set up for secure data management.

2. Setup Paper Space GPU: Sign up for a Paper Space account and provision GPU resources suitable for deep learning tasks. Install necessary dependencies and libraries like TensorFlow, PyTorch, etc., on the Paper Space instances.

3. Clone this Repository: Clone this repository to your local machine or server where you plan to perform model training.

4. Configure Environment: Set up environment variables or configuration files to specify AWS S3 bucket credentials, Paper Space API keys, and other necessary parameters.

5. Run Model Training: Use the provided scripts or notebooks to initiate model training tasks. Monitor the training process and evaluate model performance periodically.

6. Deploy Trained Models: Once training is complete and satisfactory, deploy the trained models to your Image Search Engine infrastructure for integration with other microservices.

Contributors

• Me

Feel free to contribute to this microservice by submitting pull requests, reporting issues, or suggesting improvements. Together, we can build a robust and efficient Image Search Engine powered by deep learning!