Table 5 Label conversion of orange dataset to tomato dataset: For the pseudo label obtained with different confidence thresholds, the pseudo-label self-learning method is further adapted to reduce the influence of noise in the pseudo label and generate a real tomato dataset \({\boldsymbol{D}}_{{\boldsymbol{T}}\_{\boldsymbol{tomato}}}^{\mathbf{L}}\) with higher quality labels
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 | √ | 0.1 | 0.748 | 0.748 | 0.748 | 0.725 |
√ | 0.2 | 0.757 | 0.751 | 0.751 | 0.731 | |
√ | 0.3 | 0.744 | 0.749 | 0.746 | 0.741 | |
√ | 0.4 | 0.759 | 0.757 | 0.758 | 0.744 | |
√ | 0.5 | 0.766 | 0.765 | 0.765 | 0.764 | |
√ | 0.6 | 0.769 | 0.767 | 0.768 | 0.769 | |
√ | 0.7 | 0.758 | 0.757 | 0.758 | 0.752 | |
√ | 0.8 | 0.743 | 0.747 | 0.745 | 0.748 | |
√ | 0.9 | 0.731 | 0.735 | 0.735 | 0.717 |