Datasets:
File size: 5,856 Bytes
e1bd485 71fc210 113bc1b 5ad2ae3 113bc1b d12ca86 a4628cc 741d9ed d12ca86 5ab6e68 d12ca86 b787d6c e1bd485 2e40644 66c5583 7599276 5ab6e68 7599276 99c7df7 2e40644 66c5583 a2d41f3 66c5583 a2d41f3 3c03d56 a2d41f3 66c5583 a2d41f3 5ab6e68 a2d41f3 088ef01 5ab6e68 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
---
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_01/*.png"
# - split: group_2
# path:
# - metadata.csv
# - "group_02/*.png"
# - split: group_3
# path:
# - metadata.csv
# - "group_03/*.png"
# - split: group_4
# path:
# - metadata.csv
# - "group_04/*.png"
# - split: group_5
# path:
# - metadata.csv
# - "group_05/*.png"
# - split: group_6
# path:
# - metadata.csv
# - "group_06/*.png"
# - split: group_7
# path:
# - metadata.csv
# - "group_07/*.png"
# - split: group_8
# path:
# - metadata.csv
# - "group_08/*.png"
- split: group_9
path:
- metadata.csv
- "group_09/*.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 contains 20 images for
each set, where variation is introduced by changing the position and orientation of the fish. Half the images of a set are with the fish on one side, while the other
half has the fish flipped. This structure can be seen in the dataset viewer*.
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
@misc{bengtson2025autofishdatasetbenchmarkfinegrained,
title={AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish},
author={Stefan Hein Bengtson and Daniel Lehotský and Vasiliki Ismiroglou and Niels Madsen and Thomas B. Moeslund and Malte Pedersen},
year={2025},
eprint={2501.03767},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.03767},
}
```
### Ethical Statement
Fish used in these experiments were caught and landed by fishermen following relevant legislation and normal fishing procedures.
The Danish Ministry of Food, Agriculture and Fisheries of Denmark was contacted before fish collection to ensure compliance with legislation.
The fish were dead at landing and only dead fish were included in this experiment.
There is no conflict with the European Union (EU) directive on animal experimentation (article 3, 20.10.2010, Official Journal of the European Union L276/39) and Danish law (BEK nr 12, 07/01/2016).
The laboratory facilities used at Aalborg University are approved according to relevant legislation.
___
*Due to size limitations we chose to display 2 random groups on the dataset viewer instead of the entire dataset. |