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  ---
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- dataset_info:
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- features:
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- - name: image
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- dtype: image
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- - name: objects
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- list:
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- - name: name
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- dtype: string
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- - name: boxes
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- list:
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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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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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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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  ---
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+
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+ # Brackish Underwater
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+
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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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+
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+ ## Background
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+
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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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+
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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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+
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+ ## Classes
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+
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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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+
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+ ## Splits
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+
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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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+
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+ ## Data Format
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+
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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])