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Annals of Computer Science and Information Systems, Volume 9

Position Papers of the 2016 Federated Conference on Computer Science and Information Systems

A practical study of neural network-based image classification model trained with transfer learning method

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DOI: http://dx.doi.org/10.15439/2016F211

Citation: Position Papers of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 9, pages 4956 ()

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Abstract. This paper deals with algorithms for image classification, which aim to guess ``what is on the picture'' using human-readable labels or categories. A supervised learning approach with Convolutional Neural Networks (CNNs) is studied as an effective solution to different computer vision problems, including image classification. Main contribution of this paper is a set of practical guidelines to tackle the image classification problem using publicly available tools and typical hardware platforms.


  1. Ian Goodfellow, Yoshua Bengio, and Aaron Courville, “Deep Learning”, Book in preparation for MIT Press, 2016, on-line version available at: http://www.deeplearningbook.org
  2. Michael A.Nielsen, “Neural Networks and Deep Learning”, Determination Press, 2015, on-line version of the book available at: http://neuralnetworksanddeeplearning.com/index.html
  3. LeCun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon, I., Henderson, D.,Howard, R. E., and Hubbard, W.. Handwritten digit recognition: Applications of neural network chips and automatic learning. IEEE Communications Magazine, 27(11), 1989
  4. Ch.Szegedy et al, “Going deeper with convolutions”, http://arxiv.org/abs/1409.4842
  5. ImageNet database of computer images: http://image-net.org/
  6. Yosinski J, Clune J, Bengio Y, and Lipson H. How transferable are features in deep neural networks? In Advances in Neural Information Processing Systems 27 (NIPS ’14), NIPS Foundation, 2014
  7. Caffe Model Zoo web page: https://github.com/BVLC/caffe/wiki/Model-Zoo
  8. Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo,
  9. Z. Chen, et al., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
  10. Ch.Szegedy et al., “Rethinking the Inception Architecture for Computer Vision”, http://arxiv.org/abs/1512.00567
  11. D.Kingma, J.Ba, “Adam: A Method for Stochastic Optimization”, http://arxiv.org/abs/1412.6980
  12. ODROID-XU4 hardware : http://www.hardkernel.com/main/products/prdt_info.php?g_code=G143452239825
  13. Y. LeCun, L. Bottou, G. Orr and K. Muller: Efficient BackProp, in Orr, G. and Muller K. (Eds), Neural Networks: Tricks of the trade, Springer, 1998
  14. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS 2012, Neural Information Processing Systems, Nevada, 2012