# Non-iterative optimization of pseudo-labeling thresholds for training object detection models from multiple datasets Source: [Non-iterative optimization of pseudo-labeling thresholds for training object detection models from multiple datasets]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:42:13.334Z Authors: Yuki Tanaka, Shuhei M. Yoshida, Makoto Terao Published: 2022-10-19T00:31:34.000Z ## Abstract We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets, each of which is annotated for only a subset of all the object classes. A popular approach to this problem is first to train teacher models and then to use their confident predictions as pseudo ground-truth labels when training a student model. To obtain the best result, however, thresholds for prediction confidence must be adjusted. This process typically involves iterative search and repeated training of student models and is time-consuming. Therefore, we develop a method to optimize the thresholds without iterative optimization by maximizing the $F\_\\beta$-score on a validation dataset, which measures the quality of pseudo labels and can be measured without training a student model. We experimentally demonstrate that our proposed method achieves an mAP comparable to that of grid search on the COCO and VOC datasets. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()