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lvis:一个用于大词汇量实例分割的数据集

lvis:一个用于大词汇量实例分割的数据集

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Image Search Classification

计算机视觉的核心目标是赋予算法智能描述图像的能力;目标检测是规范的图像描述任务,这在应用程序中实用性很强,并且可以直接在......

数据结构 ? 32.2G

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    README.md

    计算机视觉的核心目标是赋予算法智能描述图像的能力;目标检测是规范的图像描述任务,这在应用程序中实用性很强,并且可以直接在现有设置中进行基准测试。而物体检测器的精确度已经得到了显著提高,并且已经开发出新功能,例如:图像分割和 3D 表示。

    从少数例子中有效地学习是机器学习和计算机视觉中一个重要的开放性问题,从科学和实践的角度来看,这个机会是非常令人振奋的。但要开放这个领域进行实证研究,需要一个合适的、高质量的数据集和基准。我们的目标就是通过设计和收集 LVIS,一个用于大规模词汇量对实例分割研究基准数据集来实现这一新的研究方向,并在最终完成 164k 大小的包含 1000 类物体的约 200 万个高质量的实力分割标注图像数据集。

    Introducing LVIS

    1200+ Categories
    Found by data-driven object discovery in 164k images.
    Long Tail
    Category discovery naturally reveals a large number of rare categories.
    Masks
    More than 2 million high quality instance segmentation masks.

    LVIS Dataset

    v1.0

    Training set
    Validation set
    Test Dev
    Test Challenge

    Please see our recommended best practices for using this data.

    Note: LVIS uses the COCO 2017 train, validation, and test image sets. If you have already downloaded the COCO images, you only need to download the LVIS annotations. LVIS val set contains images from COCO 2017 train in addition to the COCO 2017 val split.

    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,
    }  

    We describe the data structures a bit more in detail below.
    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],

    }


    Categories
    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],

    }

    License

    The LVIS annotations along with this website are licensed under a Creative Commons Attribution 4.0 License. All LVIS dataset images come from the COCO dataset; please see link for their terms of use.




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