Publication detail

Deep Learning Concepts and Datasets for Image Recognition: Overview 2019

HORÁK, K.

Original Title

Deep Learning Concepts and Datasets for Image Recognition: Overview 2019

Type

conference paper

Language

English

Original Abstract

We present basics of a deep learning concept and an overview of well-known deep learning concepts as general Convolutional Neural Networks, R-CNN family, Single Shot Multibox Detector, You Only Look Once architecture and the RetinaNet in the first part of this paper. The all mentioned architectures are described to quickly compare to each other regarding their suitability for given general task. Several selected datasets often used in deep learning competitions are listed in the subsequent chapters in more details. The most known of practically used and listed datasets are COCO, KITTI, PascalVOC and CityShapes. The overview serves as a comparison of the state-of-the-art deep learning methods.

Keywords

Deep learning, dataset, image recognition, convolutional neural network, R-CNN, RetinaNet.

Authors

HORÁK, K.

Released

14. 8. 2019

Publisher

SPIE

Location

Guangzhou, China

ISBN

9781510630758

Book

Proceedings of SPIE - The International Society for Optical Engineering

Edition

Volume 11179

Edition number

Article number 11179

ISBN

0277-786X

Periodical

Proceedings of SPIE

Year of study

11179

Number

111791S

State

United States of America

Pages from

484

Pages to

491

Pages count

8

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072601173&doi=10.1117%2f12.2539806&partnerID=40&md5=250c8c0254e4037ba7340dc71d2c6f09

BibTex

@inproceedings{BUT159588,
  author="Karel {Horák}",
  title="Deep Learning Concepts and Datasets for Image Recognition: Overview 2019",
  booktitle="Proceedings of SPIE - The International Society for Optical Engineering",
  year="2019",
  series="Volume 11179",
  journal="Proceedings of SPIE",
  volume="11179",
  number="111791S",
  pages="484--491",
  publisher="SPIE",
  address="Guangzhou, China",
  doi="10.1117/12.2539806",
  isbn="9781510630758",
  issn="0277-786X",
  url="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072601173&doi=10.1117%2f12.2539806&partnerID=40&md5=250c8c0254e4037ba7340dc71d2c6f09"
}

Documents

ICDIP_2019_ID116_Horak.pdf