이를 위해 제안한 hierarchical S hifted WIN dow 방식은, 기존 self. Swin-T-YOLOv5 achieved its lowest mAP (90%) and F1-score (0.82) when detecting immature berries, where the mAP was approximately 40%, 5%, 3%, and 1% ….
Thus, an integrated, novel detection model, Swin-transformer-YOLOv5, was proposed for real-time wine grape bunch detection.The proposed method merges the Swin Transformer as a type of vision transformer and the YOLOv3 model, applying meta-learning and devising an explainable object detection model. This study proposes a Swin Transformer-based object detection model using explainable meta-learning mining.