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The impact of hyperparameter tuning on CNN performance in LEGO brick classification

Kandidat-uppsats

Jonsson, Emil, Werner, Edvin

Högskolan i Skövde/Institutionen för informationsteknologi

Publicerad: 2026

Språk: Engelska

Nyckelord

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⌕Grad-CAM⌕Convolutional Neural Network⌕hyperparameter tuning⌕LEGO bricks⌕image size⌕object classification

Sammanfattning

This study investigates how selected hyperparameter configurations affect the performance of a convolutional neural network when classifying LEGO bricks across different image datasets. The study was conducted using two LEGO brick image datasets. Dataset 1 contained rendered and photographic images and was used to evaluate different configurations of image size, batch size, learning rate, and class weights. Dataset 2 consisted of high-resolution photographs captured by the authors and was used to investigate how image size affected CNN model performance when the original image resolution was higher and more consistent.

Information

Författare
Jonsson, Emil, Werner, Edvin
Lärosäte / institution
Högskolan i Skövde/Institutionen för informationsteknologi
Publiceringsdatum
2026
Uppsatstyp
Kandidat-uppsats
Språk
Engelska
Nyckelord
⌕Grad-CAM⌕Convolutional Neural Network⌕hyperparameter tuning⌕LEGO bricks⌕image size⌕object classification

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