Deep Learning for Real-world Applications.
Exploring the challenges and solutions in deploying CNN models for real-time fish classification and object tracking in production environments. In this area we used Self-supervised machine learning (SimCLR, MOCO, BYOL) to classify the fish.
🚀How We Implemented It
We built a robust pipeline that takes real-time video feeds and processes them through our trained CNN models. The system uses self-supervised learning techniques (SimCLR, MOCO, BYOL) to improve accuracy even with limited labeled data, ensuring high-performance classification in dynamic real-world environments.
⚙️The Development Process
The development process started with data collection and preprocessing, followed by model training using self-supervised frameworks. We then optimized the models for inference speed and integrated them into a production-ready application using a microservices architecture.