• Anuradha Surabhi
• Lakshman Kumar Bendapudi

Classification of image data is more enthusiastic and challenging data mining technique. In this study, we used supervised learning mechanism, classification on color image data which consists of huge set of samples. We experiment on input data with a sequence of convolution and max pooling layers with a sequence of filters in each layers to study the local pattern matching of images. Then it is compared with a fully connected Feed Forward Neural Network architecture to better understand the performance of CNN. Convolutional Neural Networks is a Deep learning algorithm which can take image data as input, process, and classify it under certain categories.

conv2D, dense, Deep learning, flatten, image classification, keras, pooling, padding, strides, Tensorflow

"Performance Evaluation of Convolution Neural Networks on classifying Image data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.313-318, June 2019, Available :http://www.jetir.org/papers/JETIR1908847.pdf

"Performance Evaluation of Convolution Neural Networks on classifying Image data", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp313-318, June 2019, Available at : http://www.jetir.org/papers/JETIR1908847.pdf