---
task_categories:
- image-segmentation
- mask-generation
language:
- en
license: cc-by-4.0
dataset_info:
  features:
  - name: image
    dtype: image
configs:
  - config_name: example_images
    data_files:
      - split: group_1
        path:
        - metadata.csv
        - "group_1/*.png"
      - split: group_2
        path:
        - metadata.csv
        - "group_2/*.png"
      - split: group_3
        path:
        - metadata.csv
        - "group_3/*.png"
      - split: group_4
        path:
        - metadata.csv
        - "group_4/*.png"
      - split: group_5
        path:
        - metadata.csv
        - "group_5/*.png"
      - split: group_6
        path:
        - metadata.csv
        - "group_6/*.png"
      - split: group_7
        path:
        - metadata.csv
        - "group_7/*.png"
      - split: group_8
        path:
        - metadata.csv
        - "group_8/*.png"
      - split: group_9
        path:
        - metadata.csv
        - "group_9/*.png"
      - split: group_10
        path:
        - metadata.csv
        - "group_10/*.png"
      - split: group_11
        path:
        - metadata.csv
        - "group_11/*.png"
      - split: group_12
        path:
        - metadata.csv
        - "group_12/*.png"
      - split: group_13
        path:
        - metadata.csv
        - "group_13/*.png"
      - split: group_14
        path:
        - metadata.csv
        - "group_14/*.png"
      - split: group_15
        path:
        - metadata.csv
        - "group_15/*.png"
      - split: group_16
        path:
        - metadata.csv
        - "group_16/*.png"
      - split: group_17
        path:
        - metadata.csv
        - "group_17/*.png"
      - split: group_18
        path:
        - metadata.csv
        - "group_18/*.png"
      - split: group_19
        path:
        - metadata.csv
        - "group_19/*.png"
      - split: group_20
        path:
        - metadata.csv
        - "group_20/*.png"
      - split: group_21
        path:
        - metadata.csv
        - "group_21/*.png"
      - split: group_22
        path:
        - metadata.csv
        - "group_22/*.png"
      - split: group_23
        path:
        - metadata.csv
        - "group_23/*.png"
      - split: group_24
        path:
        - metadata.csv
        - "group_24/*.png"
      - split: group_25
        path:
        - metadata.csv
        - "group_25/*.png"
      # 
---

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:

|          |          |
|----------|----------|
|  <img src="example_images/1083.png" width="450px" /> | <img src="example_images/81.png" width="450px"/> |
| <img src="example_images/298.png" width="450px" /> | <img src="example_images/765.png" width="450px" /> |

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

If you use this dataset for your work, please cite:
```yaml
Citation coming soon
```
<!-- The annotations are in COCO format, with a structure as per the following example: -->
<!-- ```yaml
{
   "images": [
      {
      "height": 2056,
      "width": 2464,
      "id": 1,
      "file_name": "group_1/00001.png",
      "group": 1,
      },
      ...
    ],
   "annotations": [
      {
      "iscrowd": 0,
      "image_id": 1,
      "bbox": [],
      "segmentation": []
      "category_id": 0,
      "length": 35.5,
      "fish_id": 316,
      "side_up": "R",
      "id": 1,
      "area": 92164
      },
      ...
    ],
   "categories": [
      {
      "id": 0,
      "name": "horse_mackerel",
      "supercategory": "horse_mackerel"
      },
      ...
    ],
} -->