Politehnika i dizajn, Vol. 8 No. 1, 2020.
Stručni rad
https://doi.org/10.19279/TVZ.PD.2020-8-1-18
KLASIFIKACIJA AUTOMOBILA KORISTEĆI TRANSFERIRANO UČENJE NA RESNET ARHITEKTURI NEURONSKE MREŽE
Stjepan Ložnjak
; Tehničko veleučilište u Zagrebu, Zagreb, Hrvatska, Student
Tin Kramberger
; Tehničko veleučilište u Zagrebu, Zagreb, Hrvatska
Ivan Cesar
; Tehničko veleučilište u Zagrebu, Zagreb, Hrvatska
Renata Kramberger
; Tehničko veleučilište u Zagrebu, Zagreb, Hrvatska
Sažetak
Classification is one of the most common problems that neural networks are used for. In the case of higher resolution image classification, convolutional neural networks are commonly used. Due to the reason that convolutional neural networks are so often used in classification, there are many pretrained models that can be adapted for new domains using a technique called transfer learning. This paper shows how excellent results in classification accuracy can be achieved by applying transfer learning to pretrained convolution neural network. This paper presents the results of the learning transfer of the ResNet-152 convolution neural network on the Stanford Cars dataset. The results show accuracy over 88% only by training the last fully connected layer.
Ključne riječi
Transfer learning; ResNet; Stanford Car dataset
Hrčak ID:
242768
URI
Datum izdavanja:
17.6.2020.
Posjeta: 974 *