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

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
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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
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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