Convolutional neural network (CNN) based image classifiers always take input as an image, automatically learn its feature and classify into predefined output class. If input image resolution varies, then it hinders classification performance of CNN based image classifier. This paper proposes a methodology (training testing methods TOTV, TVTV) and presents the experimental study on the effects of varying resolution on CNN based image classification for standard image dataset MNIST and CIFAR10. The experimental result shows that degradation in resolution from higher to lower decreases performance score (accuracy, precision and F1 score) of CNN based Image classification.

Varying Resolution, Convolution Neural Network, Image Classification, Feature Learning, Classification

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