Table 4 Label conversion of orange dataset to apple dataset: for the pseudo label obtained with different confidence thresholds, the pseudo-label self-learning method is further adopted to reduce the influence of noise in the pseudo label and generate a real apple dataset \({\boldsymbol{D}}_{{\boldsymbol{T}}\_{\boldsymbol{apple}}}^{\mathbf{L}}\) 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 | 0.698 | 0.733 | 0.715 | 0.77 |
√ | 0.2 | 0.747 | 0.749 | 0.748 | 0.79 | |
√ | 0.3 | 0.765 | 0.771 | 0.768 | 0.807 | |
√ | 0.4 | 0.786 | 0.779 | 0.782 | 0.822 | |
√ | 0.5 | 0.793 | 0.802 | 0.797 | 0.828 | |
√ | 0.6 | 0.801 | 0.796 | 0.798 | 0.847 | |
√ | 0.7 | 0.828 | 0.836 | 0.832 | 0.875 | |
√ | 0.8 | 0.814 | 0.808 | 0.811 | 0.847 | |
√ | 0.9 | 0.793 | 0.801 | 0.797 | 0.838 |