Dataset Card for weed-crop-aerial

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

Dataset Summary

weed-crop-aerial

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/weed-crop-aerial

Citation Information

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

Contributions

Thanks to @mariosasko for adding this dataset.