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A Review of Deep Learning Libraries for Object Detection

Yıl 2022, Cilt: 3 Sayı: 2, 97 - 119, 26.12.2022

Öz

The idea of artificial intelligence has been in our lives since the second quarter of the 20th century. Up to now, it has enjoyed great popularity in some cases, and in others it has been forgotten. In the 2000s, the development of computer hardware and developments in artificial neural networks led to an extensive use of artificial intelligence algorithms. Deep learning studies are the pioneers of artificial intelligence studies. Deep learning is not only used in computer science and object detection, but also in many disciplines and many tasks. Different architectures, hardware and software frameworks have been developed for deep learning studies. The fact that there are so many deep learning tools and architectures can confuse the work of researchers who want to use deep learning tools to solve their problems. In this study we want to create an understanding of the concept of deep learning. We present the concept of deep learning and its subcomponents systematically. We then introduce the common deep learning frameworks that are widely used recently. A review was created from the studies in which these frameworks were examined according to different criteria such as performance, time, accuracy, searched on Google, followed by GitHub. In this study, we would like to be a guide for researchers and readers who are unfamiliar with deep learning algorithms but want to use deep learning tools in their studies.

Kaynakça

  • [1] E. Şimşek, Ö. Barış, and G. tümüklü Özyer, “Foto-kapan Görüntülerinde Hareketli Nesne Tespiti,” Erzincan Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 12, no. 2, pp. 902–919, Aug. 2019, doi: 10.18185/erzifbed.509571.
  • [2] E. Kiliç, S. Öztürk, E. Üniversitesi Mühendislik Fakültesi Bilgisayar Mühendisliği, A. Kelimeler Evrişimli Sinir Ağları, A. Sayımı, and H. Görüntüleme, “İnsansız Hava Aracı Görüntülerinde Evrişimli Sinir Ağı Kullanarak Araç Sayımı için Yeni Bir Haritalama Yöntemi.”
  • [3] Ö. Er and H. Ş. Bilge, “Bir Küçük Nesne Tespit Zorluğu Olarak Hava Görüntülerinden Araç Tespiti Vehicle Detection From Aerial Imagery As A Small Object Detection Difficulty VERİ BİLİMİ DERGİSİ www.dergipark.gov.tr/veri,” Jan. 2021. Accessed: Mar. 29, 2021. [Online]. Available: www.dergipark.gov.tr/veri.
  • [4] X. Wu, D. Sahoo, and S. C. H. Hoi, “Recent advances in deep learning for object detection,” Neurocomputing, vol. 396, pp. 39–64, 2020, doi: 10.1016/j.neucom.2020.01.085.
  • [5] A. Şeker, B. Diri, and H. H. Balık, “Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme,” Gazi Mühendislik Bilim. Derg., vol. 3, no. 3, pp. 47–64, Dec. 2017, Accessed: May 18, 2021. [Online]. Available: https://dergipark.org.tr/en/pub/gmbd/372661.
  • [6] F. Chollet, Deep Learning with Phyton. 2018.
  • [7] F. DOĞAN and İ. TÜRKOĞLU, “Derin Öğrenme Modelleri ve Uygulama Alanlarına İlişkin Bir Derleme,” DÜMF Mühendislik Derg., vol. 10, no. 2, pp. 409–445, Jun. 2019, doi: 10.24012/dumf.411130.
  • [8] V. V Nabiyev, “Yapay Zeka (6. baskı),” Ankara: Seçkin Yayıncılık, 2021.
  • [9] Prof.Dr. Çetin Elmas, Yapay Zeka Uygulamaları, 4th ed. Ankara: Seçkin Yayıncılık, 2018.
  • [10] S. Murat, B. Mühendisliği, A. Dalı, and Y. Lisans, “İNSANSIZ HAVA ARACI GÖRÜNTÜLERİNDEN DERİN ÖĞRENME YÖNTEMLERİYLE NESNE TANIMA YÜKSEK LİSANS TEZİ,” Maltepe Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2021. Accessed: May 02, 2021. [Online]. Available: http://openaccess.maltepe.edu.tr/xmlui/handle/20.500.12415/7379.
  • [11] N. Gürsakal, “Makine Öğrenmesi,” Baskı, Bursa Dora Basım Yayın Dağıtım Ltd. Şti, 2018.
  • [12] E. Öztemel, “Yapay sinir ağlari,” PapatyaYayincilik, Istanbul, 2003.
  • [13] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015, doi: 10.1038/nature14539.
  • [14] R. Elshawi, A. Wahab, A. Barnawi, and S. Sakr, “DLBench: a comprehensive experimental evaluation of deep learning frameworks,” Clust. Comput. J. Networks, Softw. Tools Appl., p. 1, 2021, doi: 10.1007/s10586-021-03240-4.
  • [15] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, Mar. 2018, vol. 2018-Janua, pp. 1–6, doi: 10.1109/ICEngTechnol.2017.8308186.
  • [16] C. Min, J. Xu, L. Xiao, D. Zhao, Y. Nie, and B. Dai, “Attentional graph neural network for parking-slot detection,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 3445–3450, Apr. 2021, doi: 10.1109/LRA.2021.3064270.
  • [17] I. Gogul and V. S. Kumar, “Flower species recognition system using convolution neural networks and transfer learning,” Oct. 2017, doi: 10.1109/ICSCN.2017.8085675.
  • [18] Ö. İnik and E. Ülker, “Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri,” Gaziosmanpaşa Bilim. Araştırma Derg., vol. ISSN, no. 6.3, 2017.
  • [19] J. S. Dramsch, “70 years of machine learning in geoscience in review,” Jun. 2020, doi: 10.1016/bs.agph.2020.08.002.
  • [20] M. Yani, B. Irawan, and C. Setiningsih, “Application of Transfer Learning Using Convolutional Neural Network Method for Early Detection of Terry’s Nail,” in Journal of Physics: Conference Series, May 2019, vol. 1201, no. 1, doi: 10.1088/1742-6596/1201/1/012052.
  • [21] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2323, 1998, doi: 10.1109/5.726791.
  • [22] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097–1105, 2012.
  • [23] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8689 LNCS, no. PART 1, pp. 818–833, doi: 10.1007/978-3-319-10590-1_53.
  • [24] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Sep. 2015, Accessed: May 18, 2021. [Online]. Available: http://www.robots.ox.ac.uk/
  • [25] G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
  • [26] A. T. KABAKUŞ, “A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study,” Sak. Univ. J. Comput. Inf. Sci., vol. 3, no. 3, pp. 169–182, Sep. 2020, doi: 10.35377/saucis.03.03.776573.
  • [27] K. Dinghofer and F. Hartung, “Analysis of Criteria for the Selection of Machine Learning Frameworks,” in 2020 International Conference on Computing, Networking and Communications, ICNC 2020, Feb. 2020, pp. 373–377, doi: 10.1109/ICNC47757.2020.9049650.
  • [28] J. Liu, Q. Huang, X. Xia, E. Shihab, D. Lo, and S. Li, “Is Using Deep Learning Frameworks Free? Characterizing Technical Debt in Deep Learning Frameworks,” in 2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), 2020, pp. 1–10.
  • [29] “Neden TensorFlow.” https://www.tensorflow.org/about?hl=tr (accessed May 19, 2021).
  • [30] M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” 2016.
  • [31] “TensorFlow Lite | Mobil ve Uç Cihazlar için Makine Öğrenimi.” https://www.tensorflow.org/lite/?hl=tr (accessed May 19, 2021).
  • [32] “Neden Keras’ı seçmelisiniz?” https://keras.io/why_keras/ (accessed May 19, 2021).
  • [33] The Theano Development Team et al., “Theano: A Python framework for fast computation of mathematical expressions,” May 2016, Accessed: May 19, 2021. [Online]. Available: http://arxiv.org/abs/1605.02688.
  • [34] M. M. Yapıcı and N. Topaloğlu, “Performance comparison of deep learning frameworks,” 2021. Accessed: May 18, 2021. [Online]. Available: https://dergipark.org.tr/tr/pub/ci.
  • [35] A. Uçar, Ö. H. Üniversitesi, and M. Bölümü, “Derin öğrenmenin Caffe kullanılarak grafik işleme kartlarında değerlendirilmesi Mehmet Safa BİNGÖL,” 2018.
  • [36] G. Al-Bdour, R. Al-Qurran, M. Al-Ayyoub, and A. Shatnawi, “Benchmarking open source deep learning frameworks,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 5479–5486, 2020, doi: 10.11591/ijece.v10i5.pp5479-5486.
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  • [39] “pytorch/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration.” https://github.com/pytorch/pytorch (accessed May 20, 2021).
  • [40] T. Chen et al., “MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems,” Dec. 2015, Accessed: May 20, 2021. [Online]. Available: http://arxiv.org/abs/1512.01274.
  • [41] S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a Next-Generation Open Source Framework for Deep Learning.”
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  • [45] S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah, “Comparative Study of Deep Learning Software Frameworks,” Nov. 2015, Accessed: May 19, 2021. [Online]. Available: http://arxiv.org/abs/1511.06435.
  • [46] Y. Wu et al., “A Comparative Measurement Study of Deep Learning as a Service Framework,” ieeexplore.ieee.org, doi: 10.1109/TSC.2019.2928551.
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Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi

Yıl 2022, Cilt: 3 Sayı: 2, 97 - 119, 26.12.2022

Öz

Yapay zekâ kavramı 1900’lü yılların ikinci çeyreğinden itibaren hayatımıza girmeye başlamıştır. Bugüne kadar ki süreçte bazen çok popüler olmuş, bazen de unutulmaya yüz tutmuştur. 2000’li yıllarda bilgisayar donanımlarının gelişmesi ve yapay sinir ağlarındaki gelişmeler Yapay zekâ araçlarının tekrardan yoğun kullanılmasına sebep olmuştur. Derin öğrenme çalışmaları yapay zekâ çalışmalarının lokomotifi konumundadır. Derin öğrenme sadece bilgisayar biliminde ve nesne algılama görevinde değil, birçok disiplinde ve birçok görevde kullanılmaktadır. Derin öğrenme çalışmaları için farklı mimariler, farklı donanımlar, farklı yazılım çerçeveleri geliştirilmiştir. Derin öğrenme araçlarının ve mimarilerinin bu kadar fazla olması özellikle problemlerinin çözümü için derin öğrenme araçlarını kullanmak isteyen araştırmacıların kafasını karıştırabilmekte veya işini zorlaştırabilmektedir. Bu çalışmada derin öğrenme kavramı hakkında bir anlayış oluşturmayı hedefliyoruz. Derin öğrenme kavramını ve onu oluşturan alt bileşenleri sistematik olarak sunuyoruz. Akabinde günümüzde yaygın olarak kullanılan ana akım derin öğrenme çerçevelerini sunuyoruz. Bu çerçevelerin performans, zaman, doğruluk, Google da aranma, GitHub’da takip edilme gibi farklı ölçütlere göre incelendiği araştırmalardan bir derleme hazırlanmıştır. Bu çalışmanın özellikle derin öğrenme araçlarına aşina olmayan ancak çalışmalarında derin öğrenme araçlarını kullanmak isteyen araştırmacı ve okuyucular için bir kılavuz olmasını arzu ediyoruz.

Kaynakça

  • [1] E. Şimşek, Ö. Barış, and G. tümüklü Özyer, “Foto-kapan Görüntülerinde Hareketli Nesne Tespiti,” Erzincan Üniversitesi Fen Bilim. Enstitüsü Derg., vol. 12, no. 2, pp. 902–919, Aug. 2019, doi: 10.18185/erzifbed.509571.
  • [2] E. Kiliç, S. Öztürk, E. Üniversitesi Mühendislik Fakültesi Bilgisayar Mühendisliği, A. Kelimeler Evrişimli Sinir Ağları, A. Sayımı, and H. Görüntüleme, “İnsansız Hava Aracı Görüntülerinde Evrişimli Sinir Ağı Kullanarak Araç Sayımı için Yeni Bir Haritalama Yöntemi.”
  • [3] Ö. Er and H. Ş. Bilge, “Bir Küçük Nesne Tespit Zorluğu Olarak Hava Görüntülerinden Araç Tespiti Vehicle Detection From Aerial Imagery As A Small Object Detection Difficulty VERİ BİLİMİ DERGİSİ www.dergipark.gov.tr/veri,” Jan. 2021. Accessed: Mar. 29, 2021. [Online]. Available: www.dergipark.gov.tr/veri.
  • [4] X. Wu, D. Sahoo, and S. C. H. Hoi, “Recent advances in deep learning for object detection,” Neurocomputing, vol. 396, pp. 39–64, 2020, doi: 10.1016/j.neucom.2020.01.085.
  • [5] A. Şeker, B. Diri, and H. H. Balık, “Derin Öğrenme Yöntemleri Ve Uygulamaları Hakkında Bir İnceleme,” Gazi Mühendislik Bilim. Derg., vol. 3, no. 3, pp. 47–64, Dec. 2017, Accessed: May 18, 2021. [Online]. Available: https://dergipark.org.tr/en/pub/gmbd/372661.
  • [6] F. Chollet, Deep Learning with Phyton. 2018.
  • [7] F. DOĞAN and İ. TÜRKOĞLU, “Derin Öğrenme Modelleri ve Uygulama Alanlarına İlişkin Bir Derleme,” DÜMF Mühendislik Derg., vol. 10, no. 2, pp. 409–445, Jun. 2019, doi: 10.24012/dumf.411130.
  • [8] V. V Nabiyev, “Yapay Zeka (6. baskı),” Ankara: Seçkin Yayıncılık, 2021.
  • [9] Prof.Dr. Çetin Elmas, Yapay Zeka Uygulamaları, 4th ed. Ankara: Seçkin Yayıncılık, 2018.
  • [10] S. Murat, B. Mühendisliği, A. Dalı, and Y. Lisans, “İNSANSIZ HAVA ARACI GÖRÜNTÜLERİNDEN DERİN ÖĞRENME YÖNTEMLERİYLE NESNE TANIMA YÜKSEK LİSANS TEZİ,” Maltepe Üniversitesi, Lisansüstü Eğitim Enstitüsü, 2021. Accessed: May 02, 2021. [Online]. Available: http://openaccess.maltepe.edu.tr/xmlui/handle/20.500.12415/7379.
  • [11] N. Gürsakal, “Makine Öğrenmesi,” Baskı, Bursa Dora Basım Yayın Dağıtım Ltd. Şti, 2018.
  • [12] E. Öztemel, “Yapay sinir ağlari,” PapatyaYayincilik, Istanbul, 2003.
  • [13] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May 27, 2015, doi: 10.1038/nature14539.
  • [14] R. Elshawi, A. Wahab, A. Barnawi, and S. Sakr, “DLBench: a comprehensive experimental evaluation of deep learning frameworks,” Clust. Comput. J. Networks, Softw. Tools Appl., p. 1, 2021, doi: 10.1007/s10586-021-03240-4.
  • [15] S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” in Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, Mar. 2018, vol. 2018-Janua, pp. 1–6, doi: 10.1109/ICEngTechnol.2017.8308186.
  • [16] C. Min, J. Xu, L. Xiao, D. Zhao, Y. Nie, and B. Dai, “Attentional graph neural network for parking-slot detection,” IEEE Robot. Autom. Lett., vol. 6, no. 2, pp. 3445–3450, Apr. 2021, doi: 10.1109/LRA.2021.3064270.
  • [17] I. Gogul and V. S. Kumar, “Flower species recognition system using convolution neural networks and transfer learning,” Oct. 2017, doi: 10.1109/ICSCN.2017.8085675.
  • [18] Ö. İnik and E. Ülker, “Derin öğrenme ve görüntü analizinde kullanılan derin öğrenme modelleri,” Gaziosmanpaşa Bilim. Araştırma Derg., vol. ISSN, no. 6.3, 2017.
  • [19] J. S. Dramsch, “70 years of machine learning in geoscience in review,” Jun. 2020, doi: 10.1016/bs.agph.2020.08.002.
  • [20] M. Yani, B. Irawan, and C. Setiningsih, “Application of Transfer Learning Using Convolutional Neural Network Method for Early Detection of Terry’s Nail,” in Journal of Physics: Conference Series, May 2019, vol. 1201, no. 1, doi: 10.1088/1742-6596/1201/1/012052.
  • [21] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2323, 1998, doi: 10.1109/5.726791.
  • [22] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv. Neural Inf. Process. Syst., vol. 25, pp. 1097–1105, 2012.
  • [23] M. D. Zeiler and R. Fergus, “Visualizing and understanding convolutional networks,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8689 LNCS, no. PART 1, pp. 818–833, doi: 10.1007/978-3-319-10590-1_53.
  • [24] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Sep. 2015, Accessed: May 18, 2021. [Online]. Available: http://www.robots.ox.ac.uk/
  • [25] G. Nguyen et al., “Machine Learning and Deep Learning frameworks and libraries for large-scale data mining: a survey,” Artif. Intell. Rev., vol. 52, pp. 77–124, 2019, doi: 10.1007/s10462-018-09679-z.
  • [26] A. T. KABAKUŞ, “A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study,” Sak. Univ. J. Comput. Inf. Sci., vol. 3, no. 3, pp. 169–182, Sep. 2020, doi: 10.35377/saucis.03.03.776573.
  • [27] K. Dinghofer and F. Hartung, “Analysis of Criteria for the Selection of Machine Learning Frameworks,” in 2020 International Conference on Computing, Networking and Communications, ICNC 2020, Feb. 2020, pp. 373–377, doi: 10.1109/ICNC47757.2020.9049650.
  • [28] J. Liu, Q. Huang, X. Xia, E. Shihab, D. Lo, and S. Li, “Is Using Deep Learning Frameworks Free? Characterizing Technical Debt in Deep Learning Frameworks,” in 2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering in Society (ICSE-SEIS), 2020, pp. 1–10.
  • [29] “Neden TensorFlow.” https://www.tensorflow.org/about?hl=tr (accessed May 19, 2021).
  • [30] M. Abadi et al., “TensorFlow: A system for large-scale machine learning,” 2016.
  • [31] “TensorFlow Lite | Mobil ve Uç Cihazlar için Makine Öğrenimi.” https://www.tensorflow.org/lite/?hl=tr (accessed May 19, 2021).
  • [32] “Neden Keras’ı seçmelisiniz?” https://keras.io/why_keras/ (accessed May 19, 2021).
  • [33] The Theano Development Team et al., “Theano: A Python framework for fast computation of mathematical expressions,” May 2016, Accessed: May 19, 2021. [Online]. Available: http://arxiv.org/abs/1605.02688.
  • [34] M. M. Yapıcı and N. Topaloğlu, “Performance comparison of deep learning frameworks,” 2021. Accessed: May 18, 2021. [Online]. Available: https://dergipark.org.tr/tr/pub/ci.
  • [35] A. Uçar, Ö. H. Üniversitesi, and M. Bölümü, “Derin öğrenmenin Caffe kullanılarak grafik işleme kartlarında değerlendirilmesi Mehmet Safa BİNGÖL,” 2018.
  • [36] G. Al-Bdour, R. Al-Qurran, M. Al-Ayyoub, and A. Shatnawi, “Benchmarking open source deep learning frameworks,” Int. J. Electr. Comput. Eng., vol. 10, no. 5, pp. 5479–5486, 2020, doi: 10.11591/ijece.v10i5.pp5479-5486.
  • [37] “The Microsoft Cognitive Toolkit - Cognitive Toolkit - CNTK | Microsoft Docs.” https://docs.microsoft.com/tr-tr/cognitive-toolkit/ (accessed May 20, 2021).
  • [38] “CNTK_2_7_Release_Notes - Bilişsel Araç Seti - CNTK | Microsoft Docs.” https://docs.microsoft.com/en-us/cognitive-toolkit/releasenotes/cntk_2_7_release_notes (accessed May 20, 2021).
  • [39] “pytorch/pytorch: Tensors and Dynamic neural networks in Python with strong GPU acceleration.” https://github.com/pytorch/pytorch (accessed May 20, 2021).
  • [40] T. Chen et al., “MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems,” Dec. 2015, Accessed: May 20, 2021. [Online]. Available: http://arxiv.org/abs/1512.01274.
  • [41] S. Tokui, K. Oono, S. Hido, and J. Clayton, “Chainer: a Next-Generation Open Source Framework for Deep Learning.”
  • [42] “Chainer – A flexible framework of neural networks — Chainer 7.7.0 documentation.” https://docs.chainer.org/en/stable/ (accessed May 20, 2021).
  • [43] “chainer/chainer: A flexible framework of neural networks for deep learning.” https://github.com/chainer/chainer (accessed May 20, 2021).
  • [44] W. Dai and D. Berleant, “Benchmarking contemporary deep learning hardware and frameworks: A survey of qualitative metrics,” in Proceedings - 2019 IEEE 1st International Conference on Cognitive Machine Intelligence, CogMI 2019, Dec. 2019, pp. 148–155, doi: 10.1109/CogMI48466.2019.00029.
  • [45] S. Bahrampour, N. Ramakrishnan, L. Schott, and M. Shah, “Comparative Study of Deep Learning Software Frameworks,” Nov. 2015, Accessed: May 19, 2021. [Online]. Available: http://arxiv.org/abs/1511.06435.
  • [46] Y. Wu et al., “A Comparative Measurement Study of Deep Learning as a Service Framework,” ieeexplore.ieee.org, doi: 10.1109/TSC.2019.2928551.
  • [47] J. Liu et al., “Usability Study of Distributed Deep Learning Frameworks For Convolutional Neural Networks,” 2018. Accessed: May 21, 2021. [Online]. Available: https://caffe2.ai/.
  • [48] S. Shi, Q. Wang, and X. Chu, “Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs,” in 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), 2018, pp. 949–957, doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000-4.
  • [49] Y. Dai, R. Zhang, R. Xue, B. Liu, and T. Li, “Towards Efficient Execution of Mainstream Deep Learning Frameworks on Mobile Devices: Architectural Implications,” IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 40, no. 3, pp. 453–466, Mar. 2020, doi: 10.1109/TCAD.2020.3003233.
  • [50] M. Mahmud, M. S. Kaiser, T. M. McGinnity, and A. Hussain, “Deep Learning in Mining Biological Data,” Cognitive Computation, vol. 13, no. 1. Springer, p. 3, Jan. 01, 2021, doi: 10.1007/s12559-020-09773-x.
Toplam 50 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Yapay Zeka
Bölüm Derlemeler
Yazarlar

Süleyman Aktürk 0000-0003-0728-247X

Kasım Serbest 0000-0002-0064-4020

Yayımlanma Tarihi 26 Aralık 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 3 Sayı: 2

Kaynak Göster

APA Aktürk, S., & Serbest, K. (2022). Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. Journal of Smart Systems Research, 3(2), 97-119.
AMA Aktürk S, Serbest K. Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. JoinSSR. Aralık 2022;3(2):97-119.
Chicago Aktürk, Süleyman, ve Kasım Serbest. “Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi”. Journal of Smart Systems Research 3, sy. 2 (Aralık 2022): 97-119.
EndNote Aktürk S, Serbest K (01 Aralık 2022) Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. Journal of Smart Systems Research 3 2 97–119.
IEEE S. Aktürk ve K. Serbest, “Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi”, JoinSSR, c. 3, sy. 2, ss. 97–119, 2022.
ISNAD Aktürk, Süleyman - Serbest, Kasım. “Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi”. Journal of Smart Systems Research 3/2 (Aralık 2022), 97-119.
JAMA Aktürk S, Serbest K. Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. JoinSSR. 2022;3:97–119.
MLA Aktürk, Süleyman ve Kasım Serbest. “Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi”. Journal of Smart Systems Research, c. 3, sy. 2, 2022, ss. 97-119.
Vancouver Aktürk S, Serbest K. Nesne Tespiti İçin Derin Öğrenme Kütüphanelerinin İncelenmesi. JoinSSR. 2022;3(2):97-119.