Abstract
The most labour-intensive stage of machine learning (ML) modelling is the appropriate preparation of correct dataset. This paper aims to show transfer dataset approach in image segmentation use case to lower labour intensity. Moreover, we test the effectiveness of this approach by training deep learning models on our prepared dataset. The models achieved high-performance metrics, even on very hard test data.
The work was supported by the EU co-funded Smart Growth Operational Programme 2014–2020 (project no. POIR.01.01.01-00-0695/19) and the dataset was provided by Allegro, Warsaw, Poland.
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Wróblewska, A., Sysko-Romańczuk, S., Prusinowski, K. (2020). Transfer Dataset in Image Segmentation Use Case. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_12
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DOI: https://doi.org/10.1007/978-3-030-63836-8_12
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