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