Datasets:

Francesco
/

uno-deck

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6,178	640	640	{ "id": [ 18534, 18535, 18536 ], "area": [ 1100, 1100, 1258 ], "bbox": [ [ 271, 212, 31, 35.5 ], [ 308, 214, 31, 35.5 ], [ 351, 225, 34, 37 ] ], "category": [ 15, 1, 13 ] }
3,006	640	640	{ "id": [ 9018, 9019, 9020 ], "area": [ 3080, 3321, 3078 ], "bbox": [ [ 321, 188, 55.5, 55.5 ], [ 372, 237, 61.5, 54 ], [ 400, 280, 57, 54 ] ], "category": [ 13, 8, 14 ] }
3,773	640	640	{ "id": [ 11319, 11320, 11321 ], "area": [ 3228, 3442, 2907 ], "bbox": [ [ 300, 121, 61.5, 52.5 ], [ 329, 156, 67.5, 51 ], [ 382, 232, 57, 51 ] ], "category": [ 8, 12, 1 ] }
4,875	640	640	{ "id": [ 14625, 14626, 14627 ], "area": [ 3071, 2493, 2469 ], "bbox": [ [ 223, 262, 52.5, 58.5 ], [ 274, 246, 47.5, 52.5 ], [ 349, 231, 44.5, 55.5 ] ], "category": [ 7, 7, 4 ] }
243	640	640	{ "id": [ 729, 730, 731 ], "area": [ 2033, 1366, 2913 ], "bbox": [ [ 295, 253, 41.5, 49 ], [ 349, 278, 38.5, 35.5 ], [ 398, 300, 52.5, 55.5 ] ], "category": [ 7, 11, 7 ] }
4,559	640	640	{ "id": [ 13677, 13678, 13679 ], "area": [ 2322, 2601, 2754 ], "bbox": [ [ 186, 243, 43, 54 ], [ 246, 256, 51, 51 ], [ 311, 283, 51, 54 ] ], "category": [ 5, 12, 14 ] }
2,910	640	640	{ "id": [ 8730, 8731, 8732 ], "area": [ 1535, 1333, 1366 ], "bbox": [ [ 400, 291, 41.5, 37 ], [ 423, 320, 43, 31 ], [ 448, 358, 38.5, 35.5 ] ], "category": [ 8, 12, 1 ] }

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Dataset Card for uno-deck

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

Dataset Summary

uno-deck

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/uno-deck

Citation Information

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

universe.roboflow.com

Point of Contact:

francesco.zuppichini@gmail.com

Size of downloaded dataset files:

619 MB

Size of the auto-converted Parquet files:

619 MB

Number of rows:

8,992