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Fibres & Textiles in Eastern Europe
Volume 30 (2022): Numero 5 (October 2022)
Accesso libero
Analysis of Clothing Image Classification Models: A Comparison Study between Traditional Machine Learning and Deep Learning Models
Jun Xu
Jun Xu
,
Yumeng Wei
Yumeng Wei
,
Aichun Wang
Aichun Wang
,
Heng Zhao
Heng Zhao
e
Damien Lefloch
Damien Lefloch
| 22 dic 2022
Fibres & Textiles in Eastern Europe
Volume 30 (2022): Numero 5 (October 2022)
INFORMAZIONI SU QUESTO ARTICOLO
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Article Category:
Research Article
Pubblicato online:
22 dic 2022
Pagine:
66 - 78
DOI:
https://doi.org/10.2478/ftee-2022-0046
Parole chiave
E-commerce
,
clothing image classification
,
traditional machine learning
,
CNN
,
HOG+SVM
,
small VGG network
© 2022 Jun Xu et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Fig. 1
Research object and methodology
Fig. 2
Flow chart of clothing image classification
Fig. 3
Schematic diagram of HOG+SVM algorithm
Fig. 4
Architecture of a simple NN
Fig. 5
Architecture of the CNN
Fig. 6
Architecture of small VGG
Fig. 7
Experimental flow chart
Fig. 8
Examples and categories of the Fashion-MNIST dataset
Fig. 9
Accuracy comparison of HOG+SVM, NN, and CNN.
Fig. 10
Example images of the Fashion144k dataset
Fig. 11
Example images of the SmallV1 dataset
Fig. 12
Recognition accuracy of HOG+SVM and Small VGG for the SmallV1 dataset
Fig. 13
Recognition accuracy of Small VGG network models in different datasets
Fig. 14
Recognition accuracy of the GhostNet model for different datasets
Fig. 15
Accuracy of Small VGG and GhostNet for different numbers of datasets
Fashion-MNIST dataset label description
Description
T-shirt/top
Trouser
Pullover
Dress
Notes
Label
0
1
2
3
4
Description
Sandal
Shirt
Sneaker
Bag
Ankle boot
Label
5
6
7
8
9
Highest accuracy comparison of different models
Name
Highest Accuracy
ML (rbf_4x4)
91.3%
CNN
89.7%
ML (Linear_4x4)
88.9%
NN
87.7%
ML (rbf_8x8)
86.7%
ML (Linear_8x8)
83.1%