U-Net for crab image semantic segmentation with PyTorch : UAV imaging and deep learning approach can id entify Brachyura in tidal flats
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2024
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| _version_ | 1866902186317840384 |
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| author | Dongwoo Kim Kwon Lee Seungwoo Son |
| author_facet | Dongwoo Kim Kwon Lee Seungwoo Son |
| contents | <p>This dataset supports the study "Identifying Brachyura in tidal flats using UAV imaging and a deep-learning approach: A Republic of Korea west coast case study" (Scientific Reports). It contains the trained U-Net semantic segmentation model and associated scripts used to detect and classify four intertidal Brachyura species — Austruca lactea, Tubuca arcuata, Macrophthalmus japonicus, and Scopimera globosa, including sex-level discrimination for the first two species — from high-resolution UAV imagery collected at the Baramarae tidal flat, Taeanhaean National Park, Republic of Korea.<br>The repository includes: (1) best_epoch.pth, the trained U-Net model weights; (2) config.py, model configuration parameters; (3) dice_loss.py, the Dice loss function used during training; (4) evaluate.py, a script for computing pixel-level accuracy metrics; (5) predict.py, a script for running species predictions on new UAV images; and (6) test.py, a script for evaluating model performance on labelled test images. Raw UAV imagery and species labels are not included owing to the endangered status of A. lactea (nationally protected in the Republic of Korea). The code is based on the PyTorch U-Net implementation (https://github.com/milesial/Pytorch-UNet).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_10589746 |
| institution | Zenodo |
| language | eng |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | U-Net for crab image semantic segmentation with PyTorch : UAV imaging and deep learning approach can id entify Brachyura in tidal flats Dongwoo Kim Kwon Lee Seungwoo Son Brachyura, Deep-learning algorithm, Ecosystem monitoring, Non-invasive monitoring, Tidal flats; UAV, U-Net model <p>This dataset supports the study "Identifying Brachyura in tidal flats using UAV imaging and a deep-learning approach: A Republic of Korea west coast case study" (Scientific Reports). It contains the trained U-Net semantic segmentation model and associated scripts used to detect and classify four intertidal Brachyura species — Austruca lactea, Tubuca arcuata, Macrophthalmus japonicus, and Scopimera globosa, including sex-level discrimination for the first two species — from high-resolution UAV imagery collected at the Baramarae tidal flat, Taeanhaean National Park, Republic of Korea.<br>The repository includes: (1) best_epoch.pth, the trained U-Net model weights; (2) config.py, model configuration parameters; (3) dice_loss.py, the Dice loss function used during training; (4) evaluate.py, a script for computing pixel-level accuracy metrics; (5) predict.py, a script for running species predictions on new UAV images; and (6) test.py, a script for evaluating model performance on labelled test images. Raw UAV imagery and species labels are not included owing to the endangered status of A. lactea (nationally protected in the Republic of Korea). The code is based on the PyTorch U-Net implementation (https://github.com/milesial/Pytorch-UNet).</p> |
| title | U-Net for crab image semantic segmentation with PyTorch : UAV imaging and deep learning approach can id entify Brachyura in tidal flats |
| topic | Brachyura, Deep-learning algorithm, Ecosystem monitoring, Non-invasive monitoring, Tidal flats; UAV, U-Net model |
| url | https://doi.org/10.5281/zenodo.10589746 |