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Multilabel Classification of Orchid Features based on Deep Learning

Post, Cassandra (2020) Multilabel Classification of Orchid Features based on Deep Learning.

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Abstract:There are many different types of orchids, with many inter-class similarities. Classifying them using the conven-tional method of comparing the features to registered orchid types is very time consuming. Thus it would be useful to have an image based orchid feature classifier. In this paper a single output multilabel classifier using transfer learning is designed to classify six different orchid features. The design is based on experiments on different loss functions, pre-trained models, number of neurons in the dense layer, dropout rates and fine-tuning. The final model uses an Xception model with one untrainable layer as feature extractor. The classifier consists of two dense layers with a dropout of 0.5 in between. The final model gets a macro average f1-score of 0.85. The model is reliable for the non-color features, however it doesn’t perform well on some rare classes of the color features.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:53 electrotechnology
Programme:Electrical Engineering BSc (56953)
Link to this item:http://purl.utwente.nl/essays/85599
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