COCO数据集有超过 200,000 张图片,80种物体类别. 所有的物体实例都用详细的分割mask进行了标注,共标注了超过 500,000 个物体实体.

{
    person  # 1
    vehicle 交通工具 #8
        {bicycle
         car
         motorcycle
         airplane
         bus
         train
         truck
         boat}
    outdoor  #5
        {traffic light
        fire hydrant
        stop sign
        parking meter
        bench}
    animal  #10
        {bird
        cat
        dog
        horse
        sheep
        cow
        elephant
        bear
        zebra
        giraffe}
    accessory 饰品 #5
        {backpack 背包
        umbrella 雨伞
        handbag 手提包
        tie 领带
        suitcase 手提箱
        }
    sports  #10
        {frisbee
        skis
        snowboard
        sports ball
        kite
        baseball bat
        baseball glove
        skateboard
        surfboard
        tennis racket
        }
    kitchen  #7
        {bottle
        wine glass
        cup
        fork
        knife
        spoon
        bowl
        }
    food  #10
        {banana
        apple
        sandwich
        orange
        broccoli
        carrot
        hot dog
        pizza
        donut
        cake
        }
    furniture 家具 #6
        {chair
        couch
        potted plant
        bed
        dining table
        toilet
        }
    electronic 电子产品 #6
        {tv
        laptop
        mouse
        remote
        keyboard
        cell phone
        }
    appliance 家用电器 #5
        {microwave
        oven
        toaster
        sink
        refrigerator
        }
    indoor  #7
        {book
        clock
        vase
        scissors
        teddy bear
        hair drier
        toothbrush
        }
}

注:

PASCAL VOC 语义类别(#20):

{
    aeroplane
    bicycle
    bird
    boat
    bottle
    bus
    car
    cat
    chair
    cow
    diningtable
    dog
    horse
    motorbike
    person
    pottedplant
    sheep
    sofa
    train
    tvmonitor
}

<h2>COCO Dataset</h2>

annotainon 数据格式:

  • object instances
  • object keypoints
  • image captions

基本数据结构如下:

{
    "info" : info, 
    "images" : [image],
    "annotations" : [annotation],
    "licenses" : [license],
}

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

image{
    "id" : int, # 图片id
    "width" : int, # 图片宽
    "height" : int, # 图片高
    "file_name" : str, # 图片名
    "license" : int,
    "flickr_url" : str,
    "coco_url" : str, # 图片链接
    "date_captured" : datetime, # 图片标注时间
}

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

<h2>Object Instance Annotations</h2>

实例标注形式:

annotation{
    "id" : int,
    "image_id" : int,
    "category_id" : int,
    "segmentation" : RLE or [polygon],
    "area" : float, 
    "bbox" : [x,y,width,height],
    "iscrowd" : 0 or 1,
}

categories[{
    "id" : int,
    "name" : str,
    "supercategory" : str,
}]

其中,

如果instance表示单个object,则iscrowd=0,segmentation=polygon; 单个object也可能需要多个polygons,比如occluded的情况下;

如果instance表示多个objecs的集合,则iscrowd=1,segmentation=RLE. iscrowd=1用于标注较多的objects,比如人群.

<h2>Object Keypoint Annotations</h2>

关键点标注形式:

annotation{
    "keypoints" : [x1,y1,v1,...],
    "num_keypoints" : int,
    "[cloned]" : ...,
}

categories[{
    "keypoints" : [str],
    "skeleton" : [edge],
    "[cloned]" : ...,
}]

关键点标注包括了物体标注的所有数据(比如 id, bbox, 等等),以及两种额外属性信息.

"keypoints"是长度为 3K 的数组,K是对某类定义的关键点总数,位置为[x,y],关键点可见性v.

如果关键点没有标注信息,则关键点位置[x=y=0],可见性v=1;

如果关键点有标注信息,但不可见,则v=2.

如果关键点在物体segment内,则认为可见.

"num_keypoints"是物体所标注的关键点数(v>0). 对于物体较多,比如物体群或者小物体时,num_keypoints=0.

对于每个类别,categories结构体数据有两种属性:"keypoints" 和 "skeleton".

"keypoints" 是长度为k的关键点名字符串;

"skeleton" 定义了关键点的连通性,主要是通过一组关键点边缘队列表的形式表示,用于可视化.

COCO现阶段仅对人体类别进行了标注.

<h2>Image Caption Annotations</h2>

图片描述/说明标注形式:

annotation{
    "id" : int,
    "image_id" : int,
    "caption" : str,
}

图片描述标注包含了图片的主题信息. 每个主题描述了特定的图片,每张图片至少有5个主题.

Last modification:October 9th, 2018 at 09:31 am