Image classification using convolutional neural network with tensorflow
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Date
2018-08
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
The present work proposes a methodology for classifying images of cats and dogs accurately, using Convolutional Neural Network and Back-Propagation. The image recognition market is estimated to expand from US$15.95 Billion in 2016 to US$38.92 Billion by 2021, at the CAGR of 19.5% between 2016 and 2021. Facebook is the largest image sharing site on Internet. Images represent the largest source of data usage on Facebook. On an average, more than 300 million images are uploaded to its site daily. The present work is aimed to develop a model for classification of cats and dogs. The work initiate with image acquisition. Then, applying image pre-processing to bring all the images in required shape and format. Proposed work is divided into two parts features learning and classification. Feature learning is done through convolutional layer and pooling layers and classification is done by Fully connected layers. The training dataset contains 12108 images. Python 3.6 is used for the programming and system is tested for 1920 images and gained the accuracy of 85.36%. The system is also validated using holdout validation techniques.
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