Learn how to implement continuous model improvement for YOLO-based posture detection on the Jetson Orin Nano—from logging predictions to automated retraining.

I tested four different YOLO 11 retraining strategies for continuous learning—and the results surprised me. Fine-tuning with frozen backbone layers achieved the best accuracy while using 70% less RAM than full retraining.

Deploy your YOLO posture model on Jetson Orin Nano using FastAPI. Build a websocket web UI, alerting system, and MacOS app. Full source code included.

Train a custom YOLO model for sitting posture detection on Jetson Orin Nano. Includes data collection, training, TensorRT optimization, and full GitHub code.