Published July 5, 2024 | Version v1

Datasets from "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks". JGR Biogeosciences.

  • 1. ROR icon Aix-Marseille Université
  • 1. Centre National de la Recherche Scientifique

Description

This repository contains the datasets linked to "A new method for the detection of siliceous microfossils on sediment microscope slides using convolutional neural networks" (Journal of Geophysical Research: Biogeosciences, https://doi.org/10.1029/2024JG008047). This includes:

  • The images and annotation* text files (in YOLO format) used for the detection of siliceous microfossils. 
  • The models and training results for the trainings presented in the main text and supporting information.

*Note that while annotations were attributed to 14 general categories, only twelve of these were used during training (i.e. Pennate, Centric, Silicoflagellate, Centric_debris, Spore, Other_Biomin, Cocco, Silicoflagellate_debris, Undetermined_silica, Chateoceros_Bacteriastrum, Calcispheres, Foraminifer), and all were pooled into a single "Microfossil" category for the purpose of the training.

Files

Baseline_model_labels.txt

Files (3.4 GB)

Name Size
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md5:1b28e4d6b0e95af636f7aff70ff0bb62
165.7 MB Download
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md5:c7df76c286673849016e42786f33c50e
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md5:553c8b9a46a92bc84fe7bf06c18b4fc9
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md5:a5413159cb85c5172b4cbd44c01311f9
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md5:c7df76c286673849016e42786f33c50e
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165.7 MB Download
md5:1d9ac4d2fd8cc365f7f6e65c42a7ccdc
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md5:c7df76c286673849016e42786f33c50e
14 Bytes Preview Download
md5:aa84ab584fe5d97a72a49b47d6f87351
165.7 MB Download
md5:0533f929551b700e66c5df623d924767
951 Bytes Preview Download
md5:c7df76c286673849016e42786f33c50e
14 Bytes Preview Download
md5:b5b329fb309e6225679c8dcda3705320
165.7 MB Download
md5:c34380a72aa6de25355eae096355a3c7
951 Bytes Preview Download
md5:323d16afda3a973d02abe129b4468ee8
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md5:793df71b3298b8effd3d98c28c170155
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md5:25555df623f8d01d37b1c951c07889ab
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md5:5f58d71b833c5f81c10835c4eddd4b6c
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md5:57994d57ec8fba3afd48f1419e38c86b
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md5:ae07be69b6266426988bc8fea623e36c
130.1 MB Preview Download

Additional details

Related works

Is metadata for
Preprint: 10.22541/essoar.170956736.63037601/v1 (DOI)
Publication: 10.1029/2024JG008047 (DOI)