Performance analysis of different optimizers for deep learning-based Image recognition

Küçük Resim Yok

Tarih

2020

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

World Scientific Publ Co Pte Ltd

Erişim Hakkı

info:eu-repo/semantics/closedAccess

Özet

Deep learning refers to Convolutional Neural Network (CNN). CNN is used for image recognition for this study. The dataset is named Fruits-360 and it is obtained from the Kaggle dataset. Seventy percent of the pictures are selected as training data and the rest of the images are used for testing. In this study, an image size is 100 x 100 x 3. Training is realized using Stochastic Gradient Descent with Momentum (sgdm), Adaptive Moment Estimation (adam) and Root Mean Square Propogation (rmsprop) techniques. The threshold value is determined as 98% for the training. When the accuracy reaches more than 98%, training is stopped. Calculation of the final validation accuracy is done using trained network. In this study, more than 98% of the predicted labels match the true labels of the validation set. Accuracies are calculated using test data for sgdm, adam and rmsprop techniques. The results are 98.08%, 98.85%, 98.88%, respectively. It is clear that fruits are recognized with good accuracy.

Açıklama

Anahtar Kelimeler

Convolutional Neural Network, Image Recognition, Sgdm Adam, Rmsprop

Kaynak

International Journal Of Pattern Recognition And Artificial Intelligence

WoS Q Değeri

Q4

Scopus Q Değeri

Q3

Cilt

34

Sayı

2

Künye