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README.md
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list: float32
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splits:
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- name: valid
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num_bytes: 60205842
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num_examples: 1238
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- name: train
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num_bytes: 483976211
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num_examples: 9967
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- name: test
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num_bytes: 60245878
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num_examples: 1239
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download_size: 537356248
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dataset_size: 604427931
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configs:
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- config_name: default
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data_files:
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- split: valid
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path: data/valid-*
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- split: train
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path: data/train-*
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- split: test
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path: data/test-*
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license: cc-by-4.0
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task_categories:
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- object-detection
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tags:
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- underwater
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- marine-life
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- brackish-water
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pretty_name: Brackish Underwater
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size_categories:
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- 10K<n<100K
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# Brackish Underwater
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An object detection dataset of underwater footage from brackish water environments in temperate waters, featuring various marine animals.
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## Background
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This dataset was introduced in the CVPR 2019 workshop paper ["Detection of Marine Animals in a New Underwater Dataset with Varying Visibility"](https://openaccess.thecvf.com/content_CVPRW_2019/html/AAMVEM/Pedersen_Detection_of_Marine_Animals_in_a_New_Underwater_Dataset_with_CVPRW_2019_paper.html) by Pedersen, Haurum, Gade, and Moeslund from Aalborg University.
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The images were captured from permanently mounted cameras in saltwater straits for long-term marine species monitoring. It is one of the first annotated underwater image datasets from temperate waters.
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## Classes
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- crab
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- fish
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- jellyfish
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- shrimp
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- small_fish
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- starfish
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## Splits
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| Split | Images |
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|-------|--------|
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| train | 9,967 |
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| test | 1,238 |
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| valid | 1,239 |
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## Data Format
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Each example contains:
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- `image`: The image (1920x1080)
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- `objects`: List of detected objects, each with:
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- `name`: Class name
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- `boxes`: Bounding boxes in normalized coordinates [x_min, y_min, x_max, y_max] (values in range [0, 1])
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