Training a Deep Learning Network with an Insignificantly Small Dataset
T. Kavitha1, K. Lakshmi2
1T.Kavitha*, Research Scholar, Department of CSE, Periyar Maniammai Institute of Science & Technology, Vallam, Thanjavur, India.
2K.Lakshmi, Professor, Department of CSE, Periyar Maniammai Institute of Science & Technology, Vallam, Thanjavur, India.
Manuscript received on January 11, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 3105-3111 | Volume-9 Issue-4, February 2020. | Retrieval Number: D2112029420/2020©BEIESP | DOI: 10.35940/ijitee.D2112.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In the last few years, Deep Learning is one of the top research areas in academia as well as in industry. Every industry is now looking for a deep learning-based solution to the problems in hand. As a researcher, learning “Deep Learning” through practical experiments will be a very challenging task. Particularly, training a deep learning network with huge amount of training data will make it impractical to do this on a normal desktop computer or laptop. Even a small-scale application in computer vision using deep learning techniques will require several days of training the deep network model on a very higher end Graphical Processing Unit (GPU) clusters or Tensor Processing Unit (TPU) clusters that makes impractical to do that research on a conventional laptop. In this work, we address the possibilities of training a deep learning network with an insignificantly small dataset. Here we mean “significantly small dataset’ as a dataset with only few images (<10) per class. Since we are going to design a prototype drone detection system which is a single class classification problem, we hereby try to train the deep learning network only with few drone images (2 images only). Our research question is: will it be possible to train a YOLO deep learning network model only with two images and achieve a descent detection accurate on a constrained test dataset of drones? This paper addresses that issue and our results prove that it is possible to train a deep learning network only with two images and achieve good performance under constrained application environments.
Keywords: Computer Vision, Convolutional Neural Networks, Deep Learning, GPU, Object Detection, TPU, Unmanned Aerial Vehicles, YOLO.
Scope of the Article: Deep Learning