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image_id
int64
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{ "id": [ 575, 576, 577 ], "area": [ 46673, 28487, 3553 ], "bbox": [ [ 168, 403, 289, 161.5 ], [ 173, 170, 215, 132.5 ], [ 66, 128, 51.5, 69 ] ], "category": [ 1, 1, 1 ] }
286
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{ "id": [ 2017, 2018, 2019 ], "area": [ 8379, 1862, 12148 ], "bbox": [ [ 135, 82, 147, 57 ], [ 0, 22, 49, 38 ], [ 226, 380, 136.5, 89 ] ], "category": [ 4, 4, 4 ] }
387
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{ "id": [ 2696, 2697 ], "area": [ 45156, 12300 ], "bbox": [ [ 0, 120, 212, 213 ], [ 204, 384, 200, 61.5 ] ], "category": [ 3, 3 ] }
100
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{ "id": [ 530 ], "area": [ 29425 ], "bbox": [ [ 167, 202, 137.5, 214 ] ], "category": [ 7 ] }
8
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{ "id": [ 61 ], "area": [ 31104 ], "bbox": [ [ 332, 286, 192, 162 ] ], "category": [ 7 ] }
188
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{ "id": [ 1299 ], "area": [ 53880 ], "bbox": [ [ 146, 237, 346.5, 155.5 ] ], "category": [ 7 ] }

Dataset Card for aquarium-qlnqy

** The original COCO dataset is stored at dataset.tar.gz**

Dataset Summary

aquarium-qlnqy

Supported Tasks and Leaderboards

  • object-detection: The dataset can be used to train a model for Object Detection.

Languages

English

Dataset Structure

Data Instances

A data point comprises an image and its object annotations.

{
  'image_id': 15,
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
  'width': 964043,
  'height': 640,
  'objects': {
    'id': [114, 115, 116, 117], 
    'area': [3796, 1596, 152768, 81002],
    'bbox': [
      [302.0, 109.0, 73.0, 52.0],
      [810.0, 100.0, 57.0, 28.0],
      [160.0, 31.0, 248.0, 616.0],
      [741.0, 68.0, 202.0, 401.0]
    ], 
    'category': [4, 4, 0, 0]
  }
}

Data Fields

  • image: the image id
  • image: PIL.Image.Image object containing the image. Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0]
  • width: the image width
  • height: the image height
  • objects: a dictionary containing bounding box metadata for the objects present on the image
    • id: the annotation id
    • area: the area of the bounding box
    • bbox: the object's bounding box (in the coco format)
    • category: the object's category.

Who are the annotators?

Annotators are Roboflow users

Additional Information

Licensing Information

See original homepage https://universe.roboflow.com/object-detection/aquarium-qlnqy

Citation Information

@misc{ aquarium-qlnqy,
    title = { aquarium qlnqy Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/aquarium-qlnqy } },
    url = { https://universe.roboflow.com/object-detection/aquarium-qlnqy },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { nov },
    note = { visited on 2023-03-29 },
}"

Contributions

Thanks to @mariosasko for adding this dataset.

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