LVIS v1.0
2D Box
2D Polygon
Common
|...
许可协议: CC BY 4.0

Overview

  • Found by data-driven object discovery in 164k images.
  • Category discovery naturally reveals a large number of rare categories.
  • More than 2 million high quality instance segmentation masks.

Data Format

LVIS has annotations for instance segmentations in a format similar to COCO. The annotations are stored using JSON. The LVIS API can be used to access and manipulate annotations. The JSON file has the following format:

{
  {
    info           : info
    images         : [images],
    annotations    : [annotations],
    licenses       : [licenses],
   }

  info{
    year            : int
    version         : str,
    description     : str,
    contributor     : str,
    url             : str,
    date_created    : datetime,
   }

  license{
    id              : int
    name            : str,
    url             : str,
   }

Images

Each image now comes with two additional fields.

  • not_exhaustive_category_ids : List of category ids which don't have all of their instances marked exhaustively.
  • neg_category_ids : List of category ids which were verified as not present in the image.
  • coco_url : Image URL. The last two path elements identify the split in the COCO dataset and the file name (e.g., http://images.cocodataset.org/train2017/000000391895.jpg). This information can be used to load the correct image from your downloaded copy of the COCO dataset.

 image{
    id                          : int
    width                       : int,
    height                      : int,
    license                     : int,
    flickr_url                  : str,
    coco_url                    : str,
    date_captured               : datetime,
    not_exhaustive_category_ids : [int],
    neg_category_ids            : [int],
  }

Category

LVIS categories are loosely based on WordNet synsets.

  • synset : Provides a unique string identifier for each category. Loosely based on WordNet synets.
  • synonyms : List of object names that belong to the same synset.
  • def : The meaning of the synset. Most of the meanings are derived from WordNet.
  • image_count : Number of images in which the category is annotated.
  • instance_count : Number of annotated instances of the category.
  • frequency : We divide the categories into three buckets based on image_count in the train set.
  categories{
     id                 : int
     synset             : str,
     synonyms           : [str],
     def                : str,
     instance_count     : int,
     image_count        : int,
     frequency          : str,
  }

Annotations

The segmentation format in LVIS is always a list of polygons.

  annotation{
   id                       : int
   image_id                 : int,
   category_id              : int,
   segmentation             : [polygon]
   area                     : float,
   bbox                     : [x,y,w,h]
  }

Citation

@inproceedings{gupta2019lvis,
  title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
  author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

License

CC BY 4.0

数据概要
数据格式
Image,
数据量
--
文件大小
25.35GB
发布方
Agrim Gupta et al.
I am a first year PhD student at Stanford University working on computer vision.
数据集反馈
| 104 | 数据量 -- | 大小 25.35GB
LVIS v1.0
2D Box 2D Polygon
Common
许可协议: CC BY 4.0

Overview

  • Found by data-driven object discovery in 164k images.
  • Category discovery naturally reveals a large number of rare categories.
  • More than 2 million high quality instance segmentation masks.

Data Format

LVIS has annotations for instance segmentations in a format similar to COCO. The annotations are stored using JSON. The LVIS API can be used to access and manipulate annotations. The JSON file has the following format:

{
  {
    info           : info
    images         : [images],
    annotations    : [annotations],
    licenses       : [licenses],
   }

  info{
    year            : int
    version         : str,
    description     : str,
    contributor     : str,
    url             : str,
    date_created    : datetime,
   }

  license{
    id              : int
    name            : str,
    url             : str,
   }

Images

Each image now comes with two additional fields.

  • not_exhaustive_category_ids : List of category ids which don't have all of their instances marked exhaustively.
  • neg_category_ids : List of category ids which were verified as not present in the image.
  • coco_url : Image URL. The last two path elements identify the split in the COCO dataset and the file name (e.g., http://images.cocodataset.org/train2017/000000391895.jpg). This information can be used to load the correct image from your downloaded copy of the COCO dataset.

 image{
    id                          : int
    width                       : int,
    height                      : int,
    license                     : int,
    flickr_url                  : str,
    coco_url                    : str,
    date_captured               : datetime,
    not_exhaustive_category_ids : [int],
    neg_category_ids            : [int],
  }

Category

LVIS categories are loosely based on WordNet synsets.

  • synset : Provides a unique string identifier for each category. Loosely based on WordNet synets.
  • synonyms : List of object names that belong to the same synset.
  • def : The meaning of the synset. Most of the meanings are derived from WordNet.
  • image_count : Number of images in which the category is annotated.
  • instance_count : Number of annotated instances of the category.
  • frequency : We divide the categories into three buckets based on image_count in the train set.
  categories{
     id                 : int
     synset             : str,
     synonyms           : [str],
     def                : str,
     instance_count     : int,
     image_count        : int,
     frequency          : str,
  }

Annotations

The segmentation format in LVIS is always a list of polygons.

  annotation{
   id                       : int
   image_id                 : int,
   category_id              : int,
   segmentation             : [polygon]
   area                     : float,
   bbox                     : [x,y,w,h]
  }

Citation

@inproceedings{gupta2019lvis,
  title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
  author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

License

CC BY 4.0

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