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