Non-iterative optimization of pseudo-labeling thresholds for training object detection models from multiple datasets

• URL: http://arxiv.org/abs/2210.10221v1
• Date: Wed, 19 Oct 2022 00:31:34 GMT
• Title: Non-iterative optimization of pseudo-labeling thresholds for training object detection models from multiple datasets
• Authors: Yuki Tanaka, Shuhei M. Yoshida, Makoto Terao
• Abstract summary: We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets. We experimentally demonstrate that our proposed method achieves an mAP comparable to that of grid search on the COCO and VOC datasets.
• Score: 2.1485350418225244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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