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ZENODO
Model . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Model . 2023
License: CC BY
Data sources: Datacite
ZENODO
Model . 2023
License: CC BY
Data sources: Datacite
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Data release of Being confident in confidence scores: calibration in deep learning models for camera trap image sequences

Authors: Dussert, Gaspard; Chamaillé-Jammes, Simon; Dray, Stéphane; Miele, Vincent;

Data release of Being confident in confidence scores: calibration in deep learning models for camera trap image sequences

Abstract

Data release of the submitted journal article : Being confident in confidence scores: calibration in deep learning models for camera trap image sequences Article is available on bioRxiv : https://www.biorxiv.org/content/10.1101/2023.11.10.566512v2 It includes : The five pretrained models An inference script : compute_features.py Metadata of the 3 test sets Cropped data of EcrinsSCJ2 datasets (Alps test set) Precomputed features for the 5 models x 3 test sets A script to compute ECE and Accuracy values : compute_calibration.py

Keywords

machine learning, classification, camera trap, event, confidence score, calibration

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average