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On October 22, 2024, 5:03:56 PM UTC,
ckan_admin:
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Added resource Otolith_Stage_1.pth to Counting using deep learning regression gives value to ecological surveys.
| f | 1 | { | f | 1 | { |
| 2 | "author": null, | 2 | "author": null, | ||
| 3 | "author_email": null, | 3 | "author_email": null, | ||
| 4 | "code": "7b.b.0c", | 4 | "code": "7b.b.0c", | ||
| 5 | "contributor": "NIOZ Royal Netherlands Institute for Sea Research", | 5 | "contributor": "NIOZ Royal Netherlands Institute for Sea Research", | ||
| 6 | "creator_user_id": "26b029ba-1ef0-4577-a666-580bc1a26c0f", | 6 | "creator_user_id": "26b029ba-1ef0-4577-a666-580bc1a26c0f", | ||
| 7 | "creators": | 7 | "creators": | ||
| 8 | tname\":\"Jeroen\",\"lastname\":\"Hoekendijk\",\"affiliation\":\"Royal | 8 | tname\":\"Jeroen\",\"lastname\":\"Hoekendijk\",\"affiliation\":\"Royal | ||
| 9 | Netherlands Institute for Sea | 9 | Netherlands Institute for Sea | ||
| 10 | ess\":\"Jeroen.hoekendijk@nioz.nl\",\"iscorrespondingauthor\":true}]", | 10 | ess\":\"Jeroen.hoekendijk@nioz.nl\",\"iscorrespondingauthor\":true}]", | ||
| 11 | "dataset_persistent_id": "DOI:10.25850/nioz/7b.b.0c", | 11 | "dataset_persistent_id": "DOI:10.25850/nioz/7b.b.0c", | ||
| 12 | "dates": "", | 12 | "dates": "", | ||
| 13 | "deposit_date": "2021-11-01", | 13 | "deposit_date": "2021-11-01", | ||
| 14 | "depositor": "Jeroen Hoekendijk", | 14 | "depositor": "Jeroen Hoekendijk", | ||
| 15 | "distribution_date": "2021-11-01", | 15 | "distribution_date": "2021-11-01", | ||
| 16 | "distributor": "Research Data Management(NIOZ Royal Netherlands | 16 | "distributor": "Research Data Management(NIOZ Royal Netherlands | ||
| 17 | Institute for Sea Research)", | 17 | Institute for Sea Research)", | ||
| 18 | "doi_date_published": "2021-12-01", | 18 | "doi_date_published": "2021-12-01", | ||
| 19 | "funding_references": "[{\"name\":\"NWO \",\"awardnumber\":\"project | 19 | "funding_references": "[{\"name\":\"NWO \",\"awardnumber\":\"project | ||
| 20 | ALWPP.2017.003\",\"awardtitle\":\"\"}]", | 20 | ALWPP.2017.003\",\"awardtitle\":\"\"}]", | ||
| 21 | "groups": [], | 21 | "groups": [], | ||
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| 23 | "isopen": false, | 23 | "isopen": false, | ||
| 24 | "license_title": null, | 24 | "license_title": null, | ||
| 25 | "maintainer": null, | 25 | "maintainer": null, | ||
| 26 | "maintainer_email": null, | 26 | "maintainer_email": null, | ||
| 27 | "metadata_created": "2024-10-22T11:45:18.547538", | 27 | "metadata_created": "2024-10-22T11:45:18.547538", | ||
| n | 28 | "metadata_modified": "2024-10-22T17:03:50.275994", | n | 28 | "metadata_modified": "2024-10-22T17:03:56.263887", |
| 29 | "name": "7bb0c", | 29 | "name": "7bb0c", | ||
| 30 | "notes": "Many ecological studies rely on count data and involve | 30 | "notes": "Many ecological studies rely on count data and involve | ||
| 31 | manual counting of objects of interest, which is time-consuming and | 31 | manual counting of objects of interest, which is time-consuming and | ||
| 32 | especially disadvantageous when time in the field or lab is limited. | 32 | especially disadvantageous when time in the field or lab is limited. | ||
| 33 | However, an increasing number of works uses digital imagery, which | 33 | However, an increasing number of works uses digital imagery, which | ||
| 34 | opens opportunities to automatise counting tasks. In this study, we | 34 | opens opportunities to automatise counting tasks. In this study, we | ||
| 35 | use machine learning to automate counting objects of interest without | 35 | use machine learning to automate counting objects of interest without | ||
| 36 | the need to label individual objects. By leveraging already existing | 36 | the need to label individual objects. By leveraging already existing | ||
| 37 | image-level annotations, this approach can also give value to | 37 | image-level annotations, this approach can also give value to | ||
| 38 | historical data that were collected and annotated over longer time | 38 | historical data that were collected and annotated over longer time | ||
| 39 | series (typical for many ecological studies), without the aim of deep | 39 | series (typical for many ecological studies), without the aim of deep | ||
| 40 | learning applications. We demonstrate deep learning regression on two | 40 | learning applications. We demonstrate deep learning regression on two | ||
| 41 | fundamentally different counting tasks: (i) daily growth rings from | 41 | fundamentally different counting tasks: (i) daily growth rings from | ||
| 42 | microscopic images of fish otolith (i.e., hearing stone) and (ii) | 42 | microscopic images of fish otolith (i.e., hearing stone) and (ii) | ||
| 43 | hauled out seals from highly variable aerial imagery. In the otolith | 43 | hauled out seals from highly variable aerial imagery. In the otolith | ||
| 44 | images, our deep learning-based regressor yields an RMSE of 3.40 | 44 | images, our deep learning-based regressor yields an RMSE of 3.40 | ||
| 45 | day-rings and an R^2 of 0.92. Initial performance in the seal images | 45 | day-rings and an R^2 of 0.92. Initial performance in the seal images | ||
| 46 | is lower (RMSE of 23.46 seals and R^2 of 0.72), which can be | 46 | is lower (RMSE of 23.46 seals and R^2 of 0.72), which can be | ||
| 47 | attributed to a lack of images with a high number of seals in the | 47 | attributed to a lack of images with a high number of seals in the | ||
| 48 | initial training set, compared to the test set. We then show how to | 48 | initial training set, compared to the test set. We then show how to | ||
| 49 | improve performance substantially (RMSE of 19.03 seals and R^2 of | 49 | improve performance substantially (RMSE of 19.03 seals and R^2 of | ||
| 50 | 0.77) by carefully selecting and relabelling just 100 additional | 50 | 0.77) by carefully selecting and relabelling just 100 additional | ||
| 51 | training images based on initial model prediction discrepancy. The | 51 | training images based on initial model prediction discrepancy. The | ||
| 52 | regression-based approach used here returns accurate counts (R^2 of | 52 | regression-based approach used here returns accurate counts (R^2 of | ||
| 53 | 0.92 and 0.77 for the rings and seals, respectively), directly usable | 53 | 0.92 and 0.77 for the rings and seals, respectively), directly usable | ||
| 54 | in ecological research.", | 54 | in ecological research.", | ||
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| 56 | "num_tags": 9, | 56 | "num_tags": 9, | ||
| 57 | "organization": { | 57 | "organization": { | ||
| 58 | "approval_status": "approved", | 58 | "approval_status": "approved", | ||
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| 65 | "name": "nioz", | 65 | "name": "nioz", | ||
| 66 | "state": "active", | 66 | "state": "active", | ||
| 67 | "title": "Royal Netherlands Institute for Sea Research", | 67 | "title": "Royal Netherlands Institute for Sea Research", | ||
| 68 | "type": "organization" | 68 | "type": "organization" | ||
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| 72 | "production_date": "2021-12-01", | 72 | "production_date": "2021-12-01", | ||
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