Deep Residual Learning for Image Classification using Cross Validation
Kshitij Tripathi1, Anil Kumar Gupta2, Rajendra G Vyas3
1Kshitij Tripathi*, Department of Computer Applications, The Maharaja Sayajirao University of Baroda, Vadodara, India.
2Anil K Gupta, Department of Computer Science & Applications, Barkatullah University, Bhopal, India.
3Rajendra G Vyas, Department of Mathematics, The Maharaja Sayajirao University of Baroda, Vadodara, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 31, 2020. | Manuscript published on April 10, 2020. | PP: 1526-1530 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4131049620/2020©BEIESP | DOI: 10.35940/ijitee.F4131.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Convolutional Neural Networks (CNN) are very common now especially in the image classification tasks as CNN’s have better classification accuracy than other techniques available in image classification. Another type of CNN called as Residual Neural Networks (RESNET) are gaining popularity because of better accuracy than normal CNN because of residual block available in it. In the present article the RESNET architecture is used for image classification on CIFAR-10 dataset using cross-validation approach that reflects a consistently better accuracy on the above dataset.
Keywords: Convolutional Neural Networks, CIFAR-10, Residual Neural Networks, Cross validation.
Scope of the Article: Neural Information Processing