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
English Title
Deep Learning Concepts and Datasets for Image Recognition: Overview 2019
Type
conference paper
Language
en
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.
English 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.
Released
14.08.2019
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
Pages from
484
Pages to
491
Pages count
8
URL
Documents
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"
}