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Designing a Lightweight Convolutional Neural Network for Onion and Weed Classification
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
2018 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The data set for this project consists of images containing onion and weed samples. It is of interest to investigate if Convolutional Neural Networks can learn to classify the crops correctly as a step in automatizing weed removal in farming. The aim of this project is to solve a classification task involving few classes with relatively few training samples (few hundred per class). Usually, small data sets are prone to overfitting, meaning that the networks generalize bad to unseen data. It is also of interest to solve the problem using small networks with low computational complexity, since inference speed is important and memory often is limited on deployable systems. This work shows how transfer learning, network pruning and quantization can be used to create lightweight networks whose classification accuracy exceeds the same architecture trained from scratch. Using these techniques, a SqueezeNet v1.1 architecture (which is already a relatively small network) can reach 1/10th of the original model size and less than half MAC operations during inference, while still maintaining a higher classification accuracy compared to a SqueezeNet v1.1 trained from scratch (96.9±1.35% vs 92.0±3.11% on 5-fold cross validation)

Place, publisher, year, edition, pages
2018. , p. 42
Series
UPTEC F, ISSN 1401-5757 ; 17050
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-341526OAI: oai:DiVA.org:uu-341526DiVA, id: diva2:1181888
External cooperation
Unibap AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2018-02-12 Created: 2018-02-10 Last updated: 2018-02-12Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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Output format
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