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Datasets:
vapaau
/
autofish

Tasks:
Image Segmentation
Mask Generation
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
Libraries:
Datasets
License:
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number_of_fish
int64
8
16
horse_mackerel
int64
0
2
whiting
int64
2
5
haddock
int64
1
4
cod
int64
0
2
hake
int64
0
3
saithe
int64
0
0
other
int64
0
0
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2
3
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The AUTOFISH dataset comprises 1500 high-quality images of fish on a conveyor belt. It features 454 unique fish with class labels, IDs, manual length measurements, and a total of 18,160 instance segmentation masks.

The fish are partitioned into 25 groups, with 14 to 24 fish in each group. Each fish only appears in one group, making it easy to create training splits. The number of fish and distribution of species in each group were pseudo-randomly selected to mimic real-world scenarios.

Every group is partitioned into three subsets: Set1, Set2, and All. Set1 and Set2 contain half of the fish each, and none of the fish overlap or touch each other. All contains all the fish from the group, purposely placed in positions with high overlap. Every group directory contains 20 images for each set, where variation is introduced by changing the position and orientation of the fish. Exactly half of every set is with the fish on their one side, while the other half has the fish flipped. The following figures display some examples with overlaid annotations:

The available classes are:

  • Cod
  • Haddock
  • Whiting
  • Hake
  • Horse mackerel
  • Other

Other information contained in the annotations:

  • Segmentation masks
  • Bounding boxes
  • Lengths
  • Unique fish IDs
  • 'Side up' referring to the side of the fish that is visible

In addition to all the labeled data, two high-overlap unlabeled groups, as well as camera calibration images are included.

You can load this dataset with a default split configuration using the datasets library

dataset = datasets.load_dataset('vapaau/autofish', revision='script', trust_remote_code=True)

If you use this dataset for your work, please cite:

Citation coming soon
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