The Current State of the Art in Multi-Label Image Classification Applied on LEGO Bricks
I.H.N. Tahur (TU Delft - Electrical Engineering, Mathematics and Computer Science)
J.C. van Gemert – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Attila Lengyel – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
Ricardo Marroquim – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
This paper shows how the current state of the art in image classification performs on LEGO bricks. Currently the standard image classification models with deep learning are single label image classifiers. In this paper we will convert them to work on multi-label images and subsequently evaluate how well they perform. We show how well the classifiers will work on three different types of datasets. Experiments will be conducted on these three types of datasets to compare the performance of three different multi-label image classifiers. The main research question accompanying this paper is ``How well does the state of the art in image classification work on LEGO bricks?''. Three subquestions are set up to answer this question. The first will regard the existence of the image classifiers. The second subquestion will regard how big the influence is of real life aspects, such as deterioration of the LEGO bricks. The final subquestion will be about the performance on the datasets. After answering these questions and conducting the experiments, we came to the conclusion that the ResNext model performed the best on almost all of the categories. Based on the numbers of the results we can also conclude that the models should perform well with multi-label images of LEGO bricks.