metadata

dataset_info:
  features:
    - name: image_id
      dtype: int64
    - name: image
      dtype: image
    - name: width
      dtype: int32
    - name: height
      dtype: int32
    - name: objects
      sequence:
        - name: id
          dtype: int64
        - name: area
          dtype: int64
        - name: bbox
          sequence: float32
          length: 4
        - name: category
          dtype:
            class_label:
              names:
                '0': aerial-pool
                '1': black-hat
                '2': bodysurface
                '3': bodyunder
                '4': umpire
                '5': white-hat
annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - en
license:
  - cc
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - object-detection
task_ids: []
pretty_name: aerial-pool
tags:
  - rf100

Dataset Card for aerial-pool

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

Dataset Description

Homepage: https://universe.roboflow.com/object-detection/aerial-pool
Point of Contact: francesco.zuppichini@gmail.com

Dataset Summary

aerial-pool

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/aerial-pool

Citation Information

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

Contributions

Thanks to @mariosasko for adding this dataset.