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Recognition of the Amazonian flora by Inception Networks with Test-time Class Prior Estimation

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F19%3A43956403" target="_blank" >RIV/49777513:23520/19:43956403 - isvavai.cz</a>

  • Result on the web

    <a href="http://ceur-ws.org/Vol-2380/paper_108.pdf" target="_blank" >http://ceur-ws.org/Vol-2380/paper_108.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Recognition of the Amazonian flora by Inception Networks with Test-time Class Prior Estimation

  • Original language description

    The paper describes an automatic system for recognition of 10,000 plant species, with focus on species from the Guiana shield and the Amazon rain forest. The proposed system achieves the best results on the PlantCLEF 2019 test set with 31.9% accuracy. Compared against human experts in plant recognition, the system performed better than 3 of the 5 participating human experts and achieved 41.0% accuracy on the subset for expert evaluation. The proposed system is based on the Inception-v4 and Inception-ResNet-v2 Convolutional Neural Network (CNN) architectures. Performance improvements were achieved by: adjusting the CNN predictions according to the estimated change of the class prior probabilities, replacing network parameters with their running averages, test-time data augmentation, filtering the provided training set and adding additional training images from GBIF.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    CEUR Workshop Proceedings Vol-2380

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    9

  • Pages from-to

    9

  • Publisher name

    CEUR-WS

  • Place of publication

    Aachen

  • Event location

    Lugano, Switzerland

  • Event date

    Sep 9, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article