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Siriporn Supratid. Impacts of layer sizes in deep residual-learning convolutional neural network on flower image classification with different class sizes. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
Impacts of layer sizes in deep residual-learning convolutional neural network on flower image classification with different class sizes
Name: Pheeraphat Sarikabuta
Organization :
Rangsit University. College of Digital Innovation Technology
Email :
peraphart1@hotmail.com
Name: Siriporn Supratid
Organization :
Rangsit University. College of Digital Innovation Technology
Email :
siriporn.su@rsu.ac.th
keyword:
Residual networks (Computer science)
LCSH:
Flowers
-- Identification
LCSH:
Machine learning
Abstract:
This paper focuses on evaluating impacts of large -, medium - and small -size deep residual-learning convolutional neural network (DRL-CNN): ResNet50, ResNet35 and ResNet17 models on classifying Oxford-102 flowers image dataset with distinct number of 10, 50 and 102 flower classes. The Flowers image classification assessments rely on precision, recall, F1 scores and accuracy rates, averaged over 10-fold cross validation to ensure unbiased experimented results. Confusion matrix is also considered for more detail of results examination. The comparison results indicate the ResNet35 yields 0.201% and 0.706% few better recognition accuracy consecutively over ResNet50 and 17 according to 10-class dataset. For 50-class, 0.060% and 0.211% bits higher accuracy of ResNet35 than ResNet50 and ResNet17 are respectively generated. Whereas, 0.040% and 0.070% a few bits better performance of ResNet35 than ResNet50 and ResNet17 are sequentially attained on 100-class one. Decreasing rate regarding superiority of ResNet35 over ResNet50 and ResNet17 is indicated when increasing class size. However, less than 0.71% higher classification performance is indicated for ResNet35 than ResNet17 for all cases within the scope of this work. Thus, ResNet17 may be preferred to ResNet35 due to approximate 33% higher amount of parameters used in ResNet35 than ResNet17.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2025-08-20
Issued:
2025-08-20
บทความ/Article
application/pdf
BibliograpyCitation :
In Electrical Engineering Academic Association (Thailand) and Rajamangala University of Technology Isan Khon Kaen Campus. Faculty of Engineering. The 2022 International Electrical Engineering Congress (iEECON 2022) (P01569). Khon Kaen : Rajamangala University of Technology Isan, 2022
eng
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Pheeraphat Sarikabuta
| Title | Contributor | Type |
|---|---|---|
| Impacts of layer sizes in deep residual-learning convolutional neural network on flower image classification with different class sizes
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Pheeraphat Sarikabuta;Siriporn Supratid | บทความ/Article |
Siriporn Supratid