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---
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task_categories:
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- image-segmentation
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- mask-generation
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language:
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- en
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---
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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, |
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and a total of 18,160 instance segmentation masks. |
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The fish are partitioned into 25 groups, with 14 to 24 fish in each group. The |
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number of fish and distribution of species in each group were pseudo-randomly selected to mimic real-world scenarios. Every group is partitioned |
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into three subsets: *Set1*, *Set2*, and *All*. *Set1* and *Set2* contain half of the fish each, and none of the |
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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 |
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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 |
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half has the fish flipped. |
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The available classes are: |
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- Cod |
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- Haddock |
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- Whiting |
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- Hake |
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- Horse mackerel |
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- Other |
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The annotations are in COCO format, with a structure as per the following example: |
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```yaml |
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{ |
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"images": [ |
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{ |
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"height": 2056, |
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"width": 2464, |
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"id": 1, |
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"file_name": "group_1/00001.png", |
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"group": 1, |
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}, |
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... |
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], |
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"annotations": [ |
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{ |
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"iscrowd": 0, |
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"image_id": 1, |
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"bbox": [], |
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"segmentation": [] |
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"category_id": 0, |
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"length": 35.5, |
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"fish_id": 316, |
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"side_up": "R", |
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"id": 1, |
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"area": 92164 |
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}, |
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... |
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], |
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"categories": [ |
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{ |
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"id": 0, |
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"name": "horse_mackerel", |
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"supercategory": "horse_mackerel" |
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}, |
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... |
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], |
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} |
