Table 2 Label conversion of orange dataset to apple dataset: the pseudo-labeling method obtaining pseudo labels by setting different confidence thresholds, generating a real apple dataset \({\boldsymbol{D}}_{{\boldsymbol{T}}\_{\boldsymbol{apple}}}^{\mathbf{L}}\) with labeling information, and finally verifying the validity of the generated labels by the model’s detection performance
From: Easy domain adaptation method for filling the species gap in deep learning-based fruit detection
Model | Pseudo label | Conf | Precision | Recall | F1 Score | mAP |
---|---|---|---|---|---|---|
Improved-Yolov3 | × | None | 0.704 | 0.658 | 0.68 | 0.653 |
√ | 0.1 | 0.724 | 0.72 | 0.722 | 0.769 | |
√ | 0.2 | 0.747 | 0.746 | 0.746 | 0.788 | |
√ | 0.3 | 0.768 | 0.769 | 0.768 | 0.805 | |
√ | 0.4 | 0.783 | 0.786 | 0.784 | 0.828 | |
√ | 0.5 | 0.79 | 0.8 | 0.795 | 0.829 | |
√ | 0.6 | 0.803 | 0.808 | 0.805 | 0.852 | |
√ | 0.7 | 0.813 | 0.822 | 0.817 | 0.845 | |
√ | 0.8 | 0.815 | 0.798 | 0.806 | 0.843 | |
√ | 0.9 | 0.79 | 0.796 | 0.793 | 0.836 |