Blender jako narzędzie do generacji danych syntetycznych

• Autorzy: Sieczka Rafał , Pańczyk Maciej

Identyfikatory

DOI

10.35784/jcsi.2086

Warianty tytułu

EN

Blender as a tool for generating synthetic data

• Języki publikacji: PL

Abstrakty

PL

Pozyskiwanie danych do treningu sieci neuronowych, jest kosztownym i pracochłonnym zadaniem, szczególnie kiedy takie dane są trudno dostępne. W niniejszym artykule zostało zaproponowane użycie programu do grafiki 3D Blender, jako narzędzia do automatycznej generacji danych syntetycznych zdjęć, na przykładzie etykiet cenowych. Przy użyciu biblioteki fastai, zostały wytrenowane klasyfikatory etykiet cenowych, na zbiorze danych syntetycznych, które porównano z klasyfikatorami trenowanymi na zbiorze danych rzeczywistych. Porównanie wyników wykazało, że możliwe jest użycie programu Blender do generacji danych syntetycznych. Pozwala to w znaczącym stopniu przyśpieszyć proces pozyskiwania danych, a co za tym idzie proces uczenia sieci neuronowych.

EN

Acquiring data for neural network training is an expensive and labour-intensive task, especially when such data is difficult to access. This article proposes the use of 3D Blender graphics software as a tool to automatically generate synthetic image data on the example of price labels. Using the fastai library, price label classifiers were trained on a set of synthetic data, which were compared with classifiers trained on a real data set. The comparison of the results showed that it is possible to use Blender to generate synthetic data. This allows for a significant acceleration of the data acquisition process and consequently, the learning process of neural networks.

Słowa kluczowe

PL

sztuczne sieci neuronowe; konwolucyjne sieci neuronowe; dane syntetyczne; blender

EN

artificial neural networks; convolutional neural network; synthetic data; blender

• Wydawca: Wydawnictwo Politechniki Lubelskiej
• Czasopismo: Journal of Computer Sciences Institute
• Rocznik: 2020
• Tom: Vol. 16
• Strony: 227--232
• Opis fizyczny: Bibliogr. 33 poz., rys., tab.

Twórcy

autor

Sieczka Rafał

• Department of Computer Science, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland

autor

Pańczyk Maciej

• Department of Computer Science, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).