five

S233

收藏
NIAID Data Ecosystem2026-03-12 收录
下载链接:
https://zenodo.org/record/3603814
下载链接
链接失效反馈
官方服务:
资源简介:
A fully annotated subset of the SO242/2_233-1 image dataset. The annotations are given as train and test splits that can be used to evaluate machine learning methods. The following classes of fauna were used for annotation: anemone coral crustacean ipnops fish litter ophiuroid other fauna sea cucumber sponge stalked crinoid For a definition of the classes see [1]. Related datasets: S083: https://doi.org/10.5281/zenodo.3600132 S155: https://doi.org/10.5281/zenodo.3603803 S171: https://doi.org/10.5281/zenodo.3603809 This dataset contains the following files: annotations/test.csv: The BIIGLE CSV annotation report of the annotations of the test split of this dataset. These annotations are used to test the performance of the trained Mask R-CNN model. annotations/train.csv: The BIIGLE CSV annotation report of the annotations of the train split of this dataset. These annotations are used to generate the annotation patches which are transformed with scale and style transfer to be used to train the Mask R-CNN model. images/: Directory that contains all the original image files. dataset.json: JSON file that contains information about the dataset. name: The name of the dataset. images_dir: Name of the directory that contains the original image files. metadata_file: Path to the CSV file that contains image metadata. test_annotations_file: Path to the CSV file that contains the test annotations. train_annotations_file: Path to the CSV file that contains the train annotations. annotation_patches_dir: Name of the directory that should contain the scale- and style-transferred annotation patches. crop_dimension: Edge length of an annotation or style patch in pixels. metadata.csv: A CSV file that contains metadata for each original image file. In this case the distance of the camera to the sea floor is given for each image.
创建时间:
2020-10-06
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作