Detail publikace

Deep Learning Concepts and Datasets for Image Recognition: Overview 2019

HORÁK, K.

Originální název

Deep Learning Concepts and Datasets for Image Recognition: Overview 2019

Anglický název

Deep Learning Concepts and Datasets for Image Recognition: Overview 2019

Jazyk

en

Originální abstrakt

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.

Anglický abstrakt

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.

Dokumenty

BibTex


@inproceedings{BUT159588,
  author="Karel {Horák}",
  title="Deep Learning Concepts and Datasets for Image Recognition: Overview 2019",
  annote="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.",
  address="SPIE",
  booktitle="Proceedings of SPIE - The International Society for Optical Engineering",
  chapter="159588",
  doi="10.1117/12.2539806",
  edition="Volume 11179",
  howpublished="online",
  institution="SPIE",
  number="111791S",
  year="2019",
  month="august",
  pages="484--491",
  publisher="SPIE",
  type="conference paper"
}