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Abstract
Image classification is a popular machine learning based applications of deep learning. Deep learning techniques are very popular because they can be effectively used in performing operations on image data in large-scale. In this paper CNN model was designed to better classify images. We make use of feature extraction part of inception v3 model for feature vector calculation and retrained the classification layer with these feature vector. By using the transfer learning mechanism the classification layer of the CNN model was trained with 20 classes of Caltech101 image dataset and 17 classes of Oxford 17 flower image dataset. After training, network was evaluated with testing dataset images from Oxford 17 flower dataset and Caltech101 image dataset. The mean testing precision of the neural network architecture with Caltech101 dataset was 98 % and with Oxford 17 Flower image dataset was 92.27 %.
Highlights
In this modern era, Computers are being powerful day by day
Few decades ago it was believed that machines are only for arithmetic operations but not for complex tasks like speech recognition, object detection, image classification, language modeling etc
Usual algorithm consisting of finite arithmetic operations cannot provide capacity to do such complex tasks for machine
Summary
Computers are being powerful day by day. They have become perfect companion with high speed computing capabilities over the time. Few decades ago it was believed that machines are only for arithmetic operations but not for complex tasks like speech recognition, object detection, image classification, language modeling etc. CNNs are applicable to fields like Speech Recognition[3], text prediction[4], handwriting generation[5]and so on In These days, RAM on a machine is cheap and is available in plenty. Defining appropriate model for image classifications which will produce good result in small training time and minimum CPU speed is the main task that this paper is intended to do
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