Efficient CNN Models for Beer Bottle Cap Classification Problem

Tran, Quan M.; Nguyen, Linh V.; Huynh, Tai; Vo, Hai H.; Pham, Vuong T.

doi:10.1007/978-3-030-35653-8_51

Cited by 5 publications

(1 citation statement)

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“…Tran et al [5] classifies close-up images of beer bottle caps with a VGG based custom model. The final model achieves a test accuracy of 99.33 % on their custom beer bottle cap dataset.…”

Section: Related Workmentioning

confidence: 99%

Classification of Beer Bottles using Object Detection and Transfer Learning

Hohlfeld¹,

Ostermeier²,

Brandl³

2022

Preprint

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Classification problems are common in Computer Vision. Despite this, there is no dedicated work for the classification of beer bottles. As part of the challenge of the master course Deep Learning, a dataset of 5207 beer bottle images and brand labels was created. An image contains exactly one beer bottle. In this paper we present a deep learning model which classifies pictures of beer bottles in a two step approach. As the first step, a Faster-R-CNN detects image sections relevant for classification independently of the brand. In the second step, the relevant image sections are classified by a ResNet-18. The image section with the highest confidence is returned as class label. We propose a model, with which we surpass the classic one step transfer learning approach and reached an accuracy of 99.86 % during the challenge on the final test dataset. We were able to achieve 100 % accuracy after the challenge ended.

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“…Tran et al [5] classifies close-up images of beer bottle caps with a VGG based custom model. The final model achieves a test accuracy of 99.33 % on their custom beer bottle cap dataset.…”

Section: Related Workmentioning

confidence: 99%