【目标检测】COCO数据集介绍

目录:COCO数据集介绍

  • 一、数据集介绍
  • 二、COCO数据集features
    • 2.1 对象检测
    • 2.2 字幕(captioning):图像的自然语言描述
    • 2.3 关键点检测
    • 2.4 图像分割(stuff image segmentation)
    • 2.5 全场景分割(panoptic:full scene segmentation)
    • 2.6 人体姿势(dense pose)估计
  • 三、其他

一、数据集介绍

COCO数据集全称为Microsoft Common Objects in Context(MS COCO),它是一个大规模(large-scale)的对象检测(object detection)、分割(segmentation)、关键点检测(key-point detection)和字幕(captioning)数据集。

此数据集由32.8万张图像组成,官网为:https://cocodataset.org/#home ,论文《Microsoft COCO: Common Objects in Context》:https://arxiv.org/pdf/1405.0312.pdf

COCO数据集的第一个版本于2014年发布,它包含16.4万张图像,分为训练集(8.3万张)、验证集(4.1万张)和测试集(4.1万张)。2015年发布了额外的8.1万张图像测试集,包括所有以前的测试图像和4万张新图像。2017年将训练集/验证集分配从8.3万/4.1万更改为11.8万/0.5万张,新的拆分使用相同的图像和标注(annotation)。2017年测试集是2015年测试集的子集包含4.1万张。此外,2017版本包含一个新的未标注的12.3万张数据集。近几年的Tasks使用的都是2017年的数据集。可以从https://cocodataset.org/#download 直接下载需要的COCO数据集。

二、COCO数据集features

2.1 对象检测

具有80个对象类别(object categories)的边界框(bounding boxes)和每个实例的分割掩码。

80个类别包括:

person, bicycle, car, motorcycle, airplane, bus, train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard, sports_ball, kite, baseball bat, baseball glove, skateboard, surfboard, tennis racket, bottle, wine glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange, broccoli, carrot, hot_dog, pizza, donut, cake, chair, couch, potted plant, bed, dining table, toilet, tv, laptop, mouse, remote, keyboard, cell phone, microwave, oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy bear, hair drier, toothbrush。

2.2 字幕(captioning):图像的自然语言描述

2.3 关键点检测

包含超过20万张图像和25万个关键点标记的人物实例

(17个可能的关键点:nose, left_eye, right_eye, left_ear, right_ear, left_shoulder, right_shoulder, left_elbow, right_elbow, left_wrist, right_wrist, left_hip, right_hip, left_knee, right_knee, left_ankle, right_ankle)。

2.4 图像分割(stuff image segmentation)

分为91种类别(stuff categories),包括:

banner, blanket, branch, bridge, building-other, bush, cabinet, cage, cardboard, carpet, ceiling-other, ceiling-tile, cloth, clothes, clouds, counter, cupboard, curtain, desk-stuff, dirt, door-stuff, fence, floor-marble, floor-other, floor-stone, floor-tile, floor-wood, flower, fog, food-other, fruit, furniture-other, grass, gravel, ground-other, hill, house, leaves, light, mat, metal, mirror-stuff, moss, mountain, mud, napkin, net, paper, pavement, pillow, plant-other, plastic, platform, playingfield, railing, railroad, river, road, rock, roof, rug, salad, sand, sea, shelf, sky-other, skyscraper, snow, solid-other, stairs, stone, straw, structural-other, table, tent, textile-other, towel, tree, vegetable, wall-brick, wall-concrete, wall-other, wall-panel, wall-stone, wall-tile, wall-wood, water-other, waterdrops, window-blind, window-other, wood。

2.5 全场景分割(panoptic:full scene segmentation)

分为80种类别(thing classes,例如人、自行车、大象)和91种stuff类别(stuff classes,例如草、天空、道路)。1(不属于任何其它类,Id为0,label name为unlabeled)+80+91

类别具体Id和对应的Label name参考:

https://github.com/nightrome/cocostuff/blob/master/labels.md 

2.6 人体姿势(dense pose)估计

超过3.9万张图像和5.6万个DensePose标注的人物实例。

三、其他

FiftyOne是一个便于可视化和访问COCO数据资源的开源工具,作为COCO模型分析的评估工具,也可用它来下载COCO数据集,FiftyOne的介绍参考:

https://blog.csdn.net/fengbingchun/article/details/121284157

COCO-2017数据集总大小约为25.20GB,在FiftyOne中你可以通过”label_types”、 “classes”、“max_samples”参数指定要下载的数据子集;可以通过”image_ids”参数指定要加载的特定图像。

详细介绍参考:

https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#dataset-zoo-coco-2017

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