Very Deep Residual Networks with MaxOut for Plant Identification in the Wild

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F16%3A00306348" target="_blank" >RIV/68407700:21230/16:00306348 - isvavai.cz</a>
Result on the web
<a href="http://ceur-ws.org/Vol-1609/16090579.pdf" target="_blank" >http://ceur-ws.org/Vol-1609/16090579.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Very Deep Residual Networks with MaxOut for Plant Identification in the Wild
Original language description
The paper presents our deep learning approach to automatic recognition of plant species from photos. We utilized a very deep 152-layer residual network model pre-trained on ImageNet, replaced the original fully connected layer with two randomly initialized fully connected layers connected with maxout, and fine-tuned the network on the PlantCLEF 2016 training data. Bagging of 3 networks was used to further improve accuracy. With the proposed approach we scored among the top 3 teams in the PlantCLEF 2016 plant identification challenge.
Czech name
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Czech description
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Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
8
Pages from-to
579-586
Publisher name
CEUR Workshop Proceedings
Place of publication
Aachen
Event location
Évora
Event date
Sep 5, 2016
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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