Abstract
Convolutional neural networks (CNNs) have proved itself a well-built model for image recognition in these modern computing days. Inclined by CNN's successes, we present an elaborative experimental assessment of CNN on image classification using a newly fabricated dataset of high-resolution images belonging to two different classes. The dataset partitioned into two distinct categories of high-resolution images of cats and dogs. This chapter presents an extensive experimental study of training size on training and validation accuracy and loss. We designed a fine-tuned predictive two-class image classification model for a large training size, which achieved a training accuracy of 100%, with validation accuracy close to 99.13%.
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Jena, B., Dash, A.K., Nayak, G.K., Mohapatra, P., Saxena, S. (2021). Image Classification for Binary Classes Using Deep Convolutional Neural Network: An Experimental Study. In: Rautaray, S.S., Pemmaraju, P., Mohanty, H. (eds) Trends of Data Science and Applications. Studies in Computational Intelligence, vol 954 . Springer, Singapore. https://doi.org/10.1007/978-981-33-6815-6_10
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DOI: https://doi.org/10.1007/978-981-33-6815-6_10
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