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image_id
int64
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{ "id": [ 456 ], "area": [ 63002 ], "bbox": [ [ 263, 167, 197.5, 319 ] ], "category": [ 4 ] }
62
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{ "id": [ 126 ], "area": [ 171925 ], "bbox": [ [ 71, 119, 509.8576354980469, 337.2034912109375 ] ], "category": [ 4 ] }
186
640
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{ "id": [ 379, 380 ], "area": [ 25465, 49350 ], "bbox": [ [ 0, 195, 198.49868774414062, 128.28884887695312 ], [ 163, 210, 248.91424560546875, 198.26458740234375 ] ], "category": [ 2, 2 ] }
211
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{ "id": [ 427 ], "area": [ 63234 ], "bbox": [ [ 230, 121, 209.02618408203125, 302.5213317871094 ] ], "category": [ 1 ] }
17
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{ "id": [ 39, 40, 41, 42, 43, 44 ], "area": [ 2826, 1, 1813, 2948, 3571, 6168 ], "bbox": [ [ 603, 89, 36, 78.5 ], [ 190, 138, 0.5, 2 ], [ 164, 122, 31, 58.5 ], [ 398, 147, 44, 67 ], [ 1, 153, 45.5, 78.5 ], [ 185, 386, 73, 84.5 ] ], "category": [ 1, 1, 1, 1, 1, 1 ] }
304
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{ "id": [ 667 ], "area": [ 116796 ], "bbox": [ [ 130, 77, 252.6085662841797, 462.36065673828125 ] ], "category": [ 4 ] }
151
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{ "id": [ 297, 298, 299, 300, 301 ], "area": [ 11242, 1833, 1501, 20779, 2676 ], "bbox": [ [ 109, 180, 109.34446716308594, 102.82148742675781 ], [ 330, 217, 37.337135314941406, 49.107486724853516 ], [ 334, 283, 40.00407409667969, 37.53800582885742 ], [ 219, 237, 102.6771240234375, 202.37879943847656 ], [ 43, 423, 40.00407409667969, 66.91557312011719 ] ], "category": [ 3, 3, 3, 3, 3 ] }
244
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640
{ "id": [ 502 ], "area": [ 117371 ], "bbox": [ [ 146, 125, 399.8241882324219, 293.55810546875 ] ], "category": [ 4 ] }
354
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{ "id": [ 813 ], "area": [ 69861 ], "bbox": [ [ 158, 252, 317.6619873046875, 219.92373657226562 ] ], "category": [ 2 ] }
250
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{ "id": [ 527 ], "area": [ 75342 ], "bbox": [ [ 198, 64, 216.5, 348 ] ], "category": [ 2 ] }

Dataset Card for cotton-20xz5

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

Dataset Summary

cotton-20xz5

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/cotton-20xz5

Citation Information

@misc{ cotton-20xz5,
    title = { cotton 20xz5 Dataset },
    type = { Open Source Dataset },
    author = { Roboflow 100 },
    howpublished = { \url{ https://universe.roboflow.com/object-detection/cotton-20xz5 } },
    url = { https://universe.roboflow.com/object-detection/cotton-20xz5 },
    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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