Transfer Learning Method for Object Detection Model Using Genetic Algorithm
Ryuji Ito*, Hajime Nobuhara*, and Shigeru Kato**
*Department of Intelligent Interaction Technologies, University of Tsukuba
1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8573, Japan
**National Institute of Technology, Niihama College
7-1 Yagumo-cho, Niihama, Ehime 792-8580, Japan
This paper proposes a transfer learning method for an object detection model using a genetic algorithm to solve the difficulty of the conventional transfer learning of deep learning-based object detection models. The genetic algorithm of the proposed method can select the re-learning layers automatically in the transfer learning process instead of a trial-and-error selection of the conventional method. Transfer learning was performed using the EfficientDet-d0 model pre-trained on the COCO dataset and the Global Wheat Head Detection (GWHD) dataset, and experiments were conducted to compare fine-tuning and the proposed method. Using the training data and the validation data of the GWHD, we compare the mean average precision (mAP) of the models trained by the conventional and the proposed methods, respectively, on the test data of the GWHD. It is confirmed that the model trained by the proposed method has higher performance than the model trained by the conventional method. The average of mAP of the proposed method, which automatically selects the re-learning layer (≈0.603), is higher than the average of mAP of the conventional method (≈0.594). Furthermore, the standard deviation of results obtained by the proposed method is smaller than that of the conventional method, and it shows the stable learning process of the proposed method.
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