no code implementations • 30 Aug 2022 • Shenglian Lu, Xiaoyu Liu, Zixaun He, Wenbo Liu, Xin Zhang, Manoj Karkee
Results showed that the proposed Swin-T-YOLOv5 outperformed all other studied models for grape bunch detection, with up to 97% of mean Average Precision (mAP) and 0. 89 of F1-score when the weather was cloudy.
