Computer Vision Accuracy Analysis with Deep Learning Model Using TensorFlow

T. Tritva Jyothi Kiran

doi:10.21276/ijircst.2020.8.4.13

Abstract

Deep learning has absolutely dominated computer vision with creating a model that most accurately classifies the given image in the dataset and surpassing human performance. In previous research works many deep learning models are created and tested for image Classification on various datasets like MNIST, CIFAR-10, ImageNet using Python. Though they got good results of Accuracy for Classification, in this paper I have extended the work of measuring the performance analysis of Accuracy for Classification and also for the Predictions on CPU and GPU using TensorFlow2.0 and Keras on CIFAR-10 dataset having 50000 images of 10 datasets having a lot of different classes with very low resolutions. TensorFlow is an emerging technology on top of Python libraries developed by Google. This work reached an Accuracy 85% on GPU of Intel® Core™ i3-7100U CPU which is acceptable with datasets used in this work are not easy to deal and all with very low resolutions having a lot of classes. That’s why it’s impacting the performance of the network. To classify and predict very low-resolution images from more datasets is really challenging one, it’s a great thing the computer vision accuracy performed excellent in my work.

Keywords

Accuracy, TensorFlow, GPU, Deep Learning, CNN, Classification, Prediction, Python, RELU, Backpropagation, Pooling, Flattening, Loss, Gradient Descent.

References

[1] Neha Sharma, Vibhor Jain, Anju Mishra, An Analysis of Convolutional Neural Networks for Image classification, Procedia Computer Science 132, 2018.

[2] Mingyuan Xin1 and Yong Wang2, Research on image classification model based on deep convolution neural network, EURASIP Journal on Image and Video Processing, 2019.

[3] Junho Yim, Jeongwoo Ju, Heechul Jung, Junmo Kim, Image Classification Using Convolutional Neural Networks with Multi-stage Feature, Robot Intelligence Technology and Applications, pp 587-59.

[4] Cristian Mateos jun-e Liu1 and Feng-Ping An, Image Classification Algorithm Based on Deep Learning-Kernel Function, 2020.

[5] D.lu & Q Weng, A survey of image classification methods and techniques for improving classification performance, 2007.

[6] Abu Sufian, Paramartha Dutta, Farhana sultana, Advancements in Image Classification using Convolutional Neural Network, 2018.

[7] Md Tohidul islam B.M. Nafiz Karim Siddique Sagidur Rahman, Taskeed Jabid, Image Recognition with Deep Learning, 2018.

[8] https://www.cs.toronto.edu/~kriz/cifar.html

[9] Barry J. Wythoff, “Backpropagation neural networks: A tutorial”.

[10] Ernest Istook, Tony Martinez, Improved backpropagation learning in neural networks with windowed momentum, International Journal of Neural Systems, vol. 12, no.3&4, pp. 303-318.

[11] C. C. Jay Kuo, Understanding Convolutional Neural Networks with A Mathematical Model, University of Southern California, Los Angeles, CA 90089-2564, 2016.

[12] Yann LeCun, LeonBottou, Yoshua Bengio, and Patrick Haffner, Gradient-Based Learning Applied to Document Recognition, PROC OF THE IEEE, NOVEMBER 1998.

[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, Microsoft Research, 2015.

[14] Dominik Scherer, Andreas Muller, and Sven Behnke, Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition, 20th International Conference on Artificial Neural Networks (ICANN), September 2010.

[15] Jianxin Wu, Introduction to Convolutional Neural Networks, LAMDA Group, National Key Lab for Novel Software Technology, Nanjing University, 2017.

[16] Y. lecu, B. boseter, J. SDenker, D Henderson, R.E. hovard, W. hubbred, AND L. Djackal, Backpropagation applied to handwritten zip code reconization, Neural Computation, VOL 1, NO.4PP.541-551, 1989.

[17] B. H. Juang, Deep neural networks a developmental perspective, APSIPA Transactions on Signal and Information Processing, 2016.

[18] F. Agostinelli, M. Hoffman, P. Sadowski, and P. Baldi, Learning activation functions to improve deep neural networks. arXiv:1412.6830, 2014.

Cites this article as

T. T. J. Kiran "Computer Vision Accuracy Analysis with Deep Learning Model Using TensorFlow", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), vol. 8, no. 4, pp. 319-325, 2020. doi: https://doi.org/10.21276/ijircst.2020.8.4.13

Corresponding Author

T. Tritva Jyothi Kiran

Assistant Professor, Computer Science Department, AKNU, Rajahmundry, Andhra Pradesh, India. (e-mail: tritvajkiran@gmail.com)

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