ZeBROD: Zero-Retraining Based Recognition and Object Detection Framework

ZeBROD is a zero‑retraining framework that tackles catastrophic forgetting in object detection by combining YOLO11n for localization with DeIT and Proxy Anchor Loss for feature extraction. It classifies products using cosine similarity against embeddings stored in a Qdrant vector database, enabling accurate detection of both new and existing items without retraining. In a retail store experiment with 140 products, ZeBROD achieved high accuracy and nearly three times faster training than traditional methods, while maintaining an inference time of 580 ms per image on an edge device.

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• rag
• computer-vision