Data underlying the publication: An integrated end-to-end deep neural network for automated detection of discarded fish species and their weight estimation.
The dataset contains images of the discarded fish on the conveyor belt and annotations. Annotations are prepared in YOLO format, i.e. separate text files, containing fish species label, object bounding box annotation, weight and occlusion level. Annotation per individual fish is written in a separate row of the file. Additionally, we provide weight file (.pt) for the best performing Detection-Weight2 model.
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| Main Authors: | , , , , , , , |
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| Format: | Dataset biblioteca |
| Published: |
Wageningen University & Research
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| Subjects: | YOLOv5, computer vision, fisheries, occlusion, weight estimation, |
| Online Access: | https://research.wur.nl/en/datasets/data-underlying-the-publication-an-integrated-end-to-end-deep-neu |
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| Summary: | The dataset contains images of the discarded fish on the conveyor belt and annotations. Annotations are prepared in YOLO format, i.e. separate text files, containing fish species label, object bounding box annotation, weight and occlusion level. Annotation per individual fish is written in a separate row of the file. Additionally, we provide weight file (.pt) for the best performing Detection-Weight2 model. |
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