Annotated marine image datasets S083, S155, S171 and S233

Citation Author(s):
Martin
Zurowietz
Biodata Mining Group, Faculty of Technology, Bielefeld University
Submitted by:
Martin Zurowietz
Last updated:
Tue, 03/03/2020 - 04:53
DOI:
10.21227/vm46-vd05
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Abstract 

Four fully annotated marine image datasets. The annotations are given as train and test splits that can be used to evaluate machine learning methods.

Instructions: 

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].

A dataset file 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.