Comparison of Convolutional Neural Network Models for Mobile Devices
Resumo
In recent years mobile devices have become an important part of our daily lives and Deep Convolutional Neural Networks have been performing well in the task of image classification. Some considerations have to be made when running a Neural Network inside a mobile device such as computational complexity and storage size. In this paper, common architectures for image classification were analyzed to retrieve the values of accuracy rate, model complexity, memory usage, and inference time. Those values were compared and it was possible to show which architecture to choose from considering mobile restrictions.
Palavras-chave:
Artificial Neural Networks, Image Recognition, Performance Evaluation, TensorFlow, Mobile
Referências
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Chen, C.-F., Lee, G. G., Sritapan, V., and Lin, C.-Y. (2016). Deep convolutional neural network on ios mobile devices. In 2016 IEEE International Workshop on Signal Processing Systems (SiPS), pages 130–135. IEEE.
Cheng, J., Wu, J., Leng, C., Wang, Y., and Hu, Q. (2017). Quantized cnn: A unified approach to accelerate and compress convolutional networks. IEEE transactions on neural networks and learning systems, 29(10):4730–4743.
Chollet, F. et al. (2015). Keras. https://keras.io.
Elhassouny, A. and Smarandache, F. (2019). Smart mobile application to recognize tomato leaf diseases using convolutional neural networks. In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), pages 1–4. IEEE.
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He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
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Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. arXiv preprint arXiv:1602.07360.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Li, X., Grandvalet, Y., and Davoine, F. (2020). A baseline regularization scheme for transfer learning with convolutional neural networks. Pattern Recognition, 98:107049.
Nielsen, M. A. (2015). Neural networks and deep learning, volume 2018. Determination press San Francisco, CA.
Qasaimeh, M., Denolf, K., Khodamoradi, A., Blott, M., Lo, J., Halder, L., Vissers, K., Zambreno, J., and Jones, P. H. (2021). Benchmarking vision kernels and neural network inference accelerators on embedded platforms. Journal of Systems Architecture, 113:101896.
Qayyum, O. and S ̧ah, M. (2018). Ios mobile application for food and location image prediction using convolutional neural networks. In 2018 IEEE 5th international conference on engineering technologies and applied sciences (ICETAS), pages 1–6. IEEE.
Qin, Z., Zhang, Z., Zhang, S., Yu, H., and Peng, Y. (2018). Merging-and-evolution networks for mobile vision applications. IEEE Access, 6:31294–31306.
Rattani, A., Reddy, N., and Derakhshani, R. (2018). Multi-biometric convolutional neural networks for mobile user authentication. In 2018 IEEE International Symposium on Technologies for Homeland Security (HST), pages 1–6. IEEE.
Rawat, W. and Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9):2352–2449.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2820–2828.
Wang, J., Markert, K., and Everingham, M. (2009). Learning models for object recognition from natural language descriptions. In BMVC, volume 1, page 2.
Yanai, K., Tanno, R., and Okamoto, K. (2016). Efficient mobile implementation of a cnn-based object recognition system. In Proceedings of the 24th ACM international conference on Multimedia, pages 362–366.
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320–3328.
Zeiler, M. D. and Fergus, R. (2014). Visualizing and understanding convolutional net- works. In European conference on computer vision, pages 818–833. Springer.
Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6848–6856.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015b). TensorFlow: Model. https://www.tensorflow.org/api_docs/python/tf/keras/Model. (accessed 17 September 2020).
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015c). TensorFlow: Transfer learning and fine-tuning. https://www.tensorflow.org/tutorials/images/transfer\_learning. (accessed 17 September 2020).
Bianco, S., Cadene, R., Celona, L., and Napoletano, P. (2018). Benchmark analysis of representative deep neural network architectures. IEEE Access, 6:64270–64277.
Chen, C.-F., Lee, G. G., Sritapan, V., and Lin, C.-Y. (2016). Deep convolutional neural network on ios mobile devices. In 2016 IEEE International Workshop on Signal Processing Systems (SiPS), pages 130–135. IEEE.
Cheng, J., Wu, J., Leng, C., Wang, Y., and Hu, Q. (2017). Quantized cnn: A unified approach to accelerate and compress convolutional networks. IEEE transactions on neural networks and learning systems, 29(10):4730–4743.
Chollet, F. et al. (2015). Keras. https://keras.io.
Elhassouny, A. and Smarandache, F. (2019). Smart mobile application to recognize tomato leaf diseases using convolutional neural networks. In 2019 International Conference of Computer Science and Renewable Energies (ICCSRE), pages 1–4. IEEE.
Fabian Pedregosa, P. G. (2011). Memory profiler, python module for monitoring memory consumption. https://github.com/pythonprofilers/memory_ profiler.
García-Alba, J., Bárcena, J. F., Ugarteburu, C., and García, A. (2019). Artificial neural networks as emulators of process-based models to analyse bathing water quality in estuaries. Water research, 150:283–295.
Guo, T. (2018). Cloud-based or on-device: An empirical study of mobile deep inference. In 2018 IEEE International Conference on Cloud Engineering (IC2E), pages 184–190. IEEE.
Haas, R. and Davies, J. (2020). What’s powering artificial intelligence? Technical report, ARM (White Paper).
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Hu, J., Shen, L., and Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7132–7141.
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., and Keutzer, K. (2016). Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size. arXiv preprint arXiv:1602.07360.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105.
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.
Li, X., Grandvalet, Y., and Davoine, F. (2020). A baseline regularization scheme for transfer learning with convolutional neural networks. Pattern Recognition, 98:107049.
Nielsen, M. A. (2015). Neural networks and deep learning, volume 2018. Determination press San Francisco, CA.
Qasaimeh, M., Denolf, K., Khodamoradi, A., Blott, M., Lo, J., Halder, L., Vissers, K., Zambreno, J., and Jones, P. H. (2021). Benchmarking vision kernels and neural network inference accelerators on embedded platforms. Journal of Systems Architecture, 113:101896.
Qayyum, O. and S ̧ah, M. (2018). Ios mobile application for food and location image prediction using convolutional neural networks. In 2018 IEEE 5th international conference on engineering technologies and applied sciences (ICETAS), pages 1–6. IEEE.
Qin, Z., Zhang, Z., Zhang, S., Yu, H., and Peng, Y. (2018). Merging-and-evolution networks for mobile vision applications. IEEE Access, 6:31294–31306.
Rattani, A., Reddy, N., and Derakhshani, R. (2018). Multi-biometric convolutional neural networks for mobile user authentication. In 2018 IEEE International Symposium on Technologies for Homeland Security (HST), pages 1–6. IEEE.
Rawat, W. and Wang, Z. (2017). Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9):2352–2449.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211–252.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1–9.
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V. (2019). Mnasnet: Platform-aware neural architecture search for mobile. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2820–2828.
Wang, J., Markert, K., and Everingham, M. (2009). Learning models for object recognition from natural language descriptions. In BMVC, volume 1, page 2.
Yanai, K., Tanno, R., and Okamoto, K. (2016). Efficient mobile implementation of a cnn-based object recognition system. In Proceedings of the 24th ACM international conference on Multimedia, pages 362–366.
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320–3328.
Zeiler, M. D. and Fergus, R. (2014). Visualizing and understanding convolutional net- works. In European conference on computer vision, pages 818–833. Springer.
Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6848–6856.
Publicado
18/07/2021
Como Citar
ISUYAMA, Vivian Kimie; ALBERTINI, Bruno de Carvalho.
Comparison of Convolutional Neural Network Models for Mobile Devices. In: WORKSHOP EM DESEMPENHO DE SISTEMAS COMPUTACIONAIS E DE COMUNICAÇÃO (WPERFORMANCE), 20. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 73-83.
ISSN 2595-6167.
DOI: https://doi.org/10.5753/wperformance.2021.15724.
