Table 3 Label conversion of orange dataset to tomato dataset: the pseudo-labeling method obtaining pseudo labels by setting different confidence thresholds, generating a real tomato dataset \({\boldsymbol{D}}_{{\boldsymbol{T}}\_{\boldsymbol{tomato}}}^{\mathbf{L}}\) with labeling information, and finally verifying the validity of the generated labels by the model 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 | 0.723 | 0.725 | 0.724 | 0.711 |
√ | 0.1 | 0.753 | 0.751 | 0.752 | 0.729 | |
√ | 0.2 | 0.754 | 0.756 | 0.755 | 0.732 | |
√ | 0.3 | 0.745 | 0.747 | 0.746 | 0.738 | |
√ | 0.4 | 0.769 | 0.767 | 0.768 | 0.741 | |
√ | 0.5 | 0.77 | 0.769 | 0.769 | 0.752 | |
√ | 0.6 | 0.765 | 0.765 | 0.765 | 0.748 | |
√ | 0.7 | 0.76 | 0.759 | 0.759 | 0.745 | |
√ | 0.8 | 0.748 | 0.747 | 0.748 | 0.744 | |
√ | 0.9 | 0.705 | 0.708 | 0.707 | 0.688 |