Deep Transform Learning Vision Accuracy Analysis on GPU using Tensor Flow
T. Tritva Jyothi Kiran

T. Tritva Jyothi Kiran, Assistant Professor, Department of Computer Science, AKNU, Rajahmundry, (A.P.), India. 

Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 224-227 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4402099320 | DOI: 10.35940/ijrte.C4402.099320
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

Abstract: Transfer learning is one of the most amazing concepts in machine learning and A.I. Transfer learning is completely unsupervised model. Transfer learning is a machine learning technique in which a network that has been trained to perform a specific task is being reused or repurposed as a starting point to perform another similar task. For this work I used ImageNet Dataset and MobileNet model to analyse Accuracy performance of my Deep Transform learning model on GPU of Intel® Core™ i3-7100U CPU using TensorFlow 2.0 Hub and Keras. ImageNet is an open source Large-Scale dataset of images consisting of 1000 classes and over 1.5 million images. And my overall idea is to analyse accuracy of Vision performance on the very poor network configuration. This work reached an Accuracy almost near to 100% on GPU of Intel® Core™ i3-7100U CPU which is great result with datasets used in this work are not easy to deal and having a lot of classes. That’s why it’s impacting the performance of the network. To classify and predict from tons of images from more classes on low configured network is really challenging one, it’s a great thing the computer vision accuracy showed an excellent vision nearly 100% on GPU in my work. 
Keywords: Accuracy, Vision, TensorFlow, Transform Learning, Deep Learning, GPU, Dense layer, ImageNet Database.