Project Overview
Data collection, preprocessing is a tedious process, definitely it takes a lot of time to collect good quality data, preprocees them (especialy image data of specific usecase ). To tackle this problem, I architected the pipeline, led, and developed a cross-platform Flutter application designed to streamline the data collection pipeline. The app enables users to acquire data and feed it into a model in just two simple steps, optimizing efficiency and usability. In the end, users (in our case robots) get the trained model ready for edge deployment.
Workflow
Key Features
- Cross-Platform Compatibility: Developed using Flutter, ensuring seamless performance on both iOS and Android.
- Streamlined Data Collection: Users can collect, label, and process data efficiently.
- Automated Data Pipeline: The app integrates with cloud-based services to automate data processing and model training.
- Minimal User Interaction: Designed to simplify the process, requiring only two steps to send data to the model.
- Scalable Architecture: Built to support various data types and models for future adaptability.
- End-to-End Model Generation: The pipeline not only processes data but also delivers a trained model at the end.
Technologies Used
- Software Architecture: PlantUML.
- Flutter (Dart) for cross-platform development
- Firebase for cloud storage and backend services
- Paperspace for fetching and training the data to produce the vision model.
- CI/CD Pipelines for automated testing and deployment
Note: App is not open source and publicly not available in Appstore (Android or IOS), its an internal proprietory tool, for optimizing the data collection process.