Learn from Foundation Model: Fruit Detection Model without Manual Annotation

ArXi:2411.16196v2 Announce Type: replace-cross Recent breakthroughs in large foundation models have enabled the possibility of transferring knowledge pre-trained on vast datasets to domains with limited data availability. Agriculture is one of the domains that lacks sufficient data. This study proposes a framework to train effective, domain-specific, small models from foundation models without manual annotation.