Performance Analysis of Different Optimizers for Deep Learning-Based Image Recognition
| Autor: | Seda Postalcioglu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
business.industry
Computer science Deep learning 02 engineering and technology Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 34:2051003 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001420510039 |
| Popis: | 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 [Formula: see text]. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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