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Comparison of pre-trained Convolutional Neural Network (CNN) architectures for classification of organic and recyclable materials from solid waste.
2022 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Place, publisher, year, edition, pages
2022. , p. 38
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:bth-23867OAI: oai:DiVA.org:bth-23867DiVA, id: diva2:1710028
Subject / course
DV1478 Bachelor Thesis in Computer Science
Educational program
DVGDT Bachelor Qualification Plan in Computer Science 60.0 hp
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Available from: 2022-11-16 Created: 2022-11-10 Last updated: 2022-11-16Bibliographically approved

Open Access in DiVA

Comparison of pre-trained Convolutional Neural Network (CNN) architectures for classification of organic and recyclable materials from solid waste.(559 kB)1161 downloads
File information
File name FULLTEXT02.pdfFile size 559 kBChecksum SHA-512
a2c62cad79a99533111f92526c3cfc968f83b782b0865c381271e17725304a59b5c5d26a40009529d2596251bd3b2f67e85a0fc955197ff47a394279181e4314
Type fulltextMimetype application/pdf

Computer Sciences

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
  • rtf