COCO数据集

AI/ 数据集 demo
创建时间:
字数:3.3k 阅读:
  1. 数据集文件夹介绍
  2. 文件内容
    1. 1、annotations_trainval2017.zip
    2. 2、image_info_test2017.zip
    3. 3、stuff_annotations_trainval2017.zip
    4. 4、test2017.zip
    5. 5、train2017.zip
    6. 6、val2017.zip
  3. JSON文件解析
    1. info字段
    2. images字段
    3. licenses字段
    4. annotations字段
  4. 实例文档
  5. 帮助文档

数据集文件夹介绍

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COCO2017
├──annotations_trainval2017.zip
├──image_info_test2017.zip
├──stuff_annotations_trainval2017.zip
├──test2017.zip
├──train2017.zip
├──val2017.zip

下载地址
http://images.cocodataset.org/zips/train2017.zip
http://images.cocodataset.org/zips/val2017.zip
http://images.cocodataset.org/zips/test2017.zip
http://images.cocodataset.org/annotations/stuff_annotations_trainval2017.zip
http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip
http://images.cocodataset.org/zips/unlabeled2017.zip

文件内容

以下是每个文件内部的文件说明

1、annotations_trainval2017.zip

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captions_train2017.json   # 看图说话
captions_val2017.json     # 看图说话
instances_train2017.json  # 目标检测
instances_val2017.json    # 目标检测
person_keypoints_train2017.json  # 目标上的关键点
person_keypoints_val2017.json    # 目标上的关键点

2、image_info_test2017.zip

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image_info_test2017.json
image_info_test-dev2017.json

3、stuff_annotations_trainval2017.zip

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deprecated-challenge2017
├──train-ids.txt
└──val-ids.txt
stuff_train2017.json
stuff_train2017_pixelmaps.zip
stuff_val2017.json
stuff_val2017_pixelmaps.zip

4、test2017.zip

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000000000001.jpg
000000000116.jpg
……

5、train2017.zip

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000000000201.jpg
000000000316.jpg
……

6、val2017.zip

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000000000401.jpg
000000000516.jpg
……

JSON文件解析

instances_train2017.json、instances_val2017.json文件解析
Object Instance这种格式的文件从头至尾按照顺序分为以下段落：

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

你打开这两个文件，虽然内容很多，但从文件开始到结尾按照顺序就是这5段。在不同的JSON文件中info、licenses、images是一样的。不同的是annotation和category这两种结构体，他们在不同类型的JSON文件中是不一样的。

images数组元素的数量等同于划入训练集（或者测试集）的图片的数量；

annotations数组元素的数量等同于训练集（或者测试集）中bounding box的数量；

categories数组元素的数量为80（2017年）；

info字段

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info{
    "year": int,
    "version": str,
    "description": str,
    "contributor": str,
    "url": str,
    "date_created": datetime,
}
# info的一个实例
"info":{
	"description":"This is stable 1.0 version of the 2014 MS COCO dataset.",
	"url":"http:\/\/mscoco.org",
	"version":"1.0","year":2014,
	"contributor":"Microsoft COCO group",
	"date_created":"2015-01-27 09:11:52.357475"
},

images字段

Images是包含多个image实例的数组，对于一个image类型的实例

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image{
    "id": int,
    "width": int,
    "height": int,
    "file_name": str,
    "license": int,
    "flickr_url": str,
    "coco_url": str,
    "date_captured": datetime,
}
# images实例
{
	"license":3,
	"file_name":"COCO_val2014_000000391895.jpg",
	"coco_url":"http:\/\/mscoco.org\/images\/391895",
	"height":360,"width":640,"date_captured":"2013-11-14 11:18:45",
	"flickr_url":"http:\/\/farm9.staticflickr.com\/8186\/8119368305_4e622c8349_z.jpg",
	"id":391895
},

licenses字段

licenses是包含多个license实例的数组，对于一个license类型的实例

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license{
    "id": int,
    "name": str,
    "url": str,
} 
licenses实例
{
	"url":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/2.0\/",
	"id":1,
	"name":"Attribution-NonCommercial-ShareAlike License"
},

annotations字段

annotations字段是包含多个annotation实例的一个数组，annotation类型本身又包含了一系列的字段，如这个目标的category id和segmentation mask。segmentation格式取决于这个实例是一个单个的对象（即iscrowd=0，将使用polygons格式）还是一组对象（即iscrowd=1，将使用RLE格式）。如下所示：

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

注意，单个的对象（iscrowd=0)可能需要多个polygon来表示，比如这个对象在图像中被挡住了。而iscrowd=1时（将标注一组对象，比如一群人）的segmentation使用的就是RLE格式。

注意啊，只要是iscrowd=0那么segmentation就是polygon格式；只要iscrowd=1那么segmentation就是RLE格式。另外，每个对象（不管是iscrowd=0还是iscrowd=1）都会有一个矩形框bbox ，矩形框左上角的坐标和矩形框的长宽会以数组的形式提供，数组第一个元素就是左上角的横坐标值。

area是area of encoded masks，是标注区域的面积。如果是矩形框，那就是高乘宽；如果是polygon或者RLE，那就复杂点。

最后，annotation结构中的categories字段存储的是当前对象所属的category的id，以及所属的supercategory的name。

下面是从instances_val2017.json文件中摘出的一个annotation的实例，这里的segmentation就是polygon格式：

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{
	"segmentation": [[510.66,423.01,511.72,420.03,510.45......]],
	"area": 702.1057499999998,
	"iscrowd": 0,
	"image_id": 289343,
	"bbox": [473.07,395.93,38.65,28.67],
	"category_id": 18,
	"id": 1768
},

实例文档

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{
	"info": {
		"description": "COCO 2017 Dataset",
		"url": "http://cocodataset.org",
		"version": "1.0",
		"year": 2017,
		"contributor": "COCO Consortium",
		"date_created": "2017/09/01"
	},
	"licenses": [{
			"url": "http://creativecommons.org/licenses/by-nc-sa/2.0/",
			"id": 1,
			"name": "Attribution-NonCommercial-ShareAlike License"
		},
		{
			"url": "http://creativecommons.org/licenses/by-nc/2.0/",
			"id": 2,
			"name": "Attribution-NonCommercial License"
		},
		{
			"url": "http://creativecommons.org/licenses/by-nc-nd/2.0/",
			"id": 3,
			"name": "Attribution-NonCommercial-NoDerivs License"
		},
		{
			"url": "http://creativecommons.org/licenses/by/2.0/",
			"id": 4,
			"name": "Attribution License"
		},
		{
			"url": "http://creativecommons.org/licenses/by-sa/2.0/",
			"id": 5,
			"name": "Attribution-ShareAlike License"
		},
		{
			"url": "http://creativecommons.org/licenses/by-nd/2.0/",
			"id": 6,
			"name": "Attribution-NoDerivs License"
		},
		{
			"url": "http://flickr.com/commons/usage/",
			"id": 7,
			"name": "No known copyright restrictions"
		},
		{
			"url": "http://www.usa.gov/copyright.shtml",
			"id": 8,
			"name": "United States Government Work"
		}
	],
	"images": [{
			"license": 4,
			"file_name": "000000397133.jpg",
			"coco_url": "http://images.cocodataset.org/val2017/000000397133.jpg",
			"height": 427,
			"width": 640,
			"date_captured": "2013-11-14 17:02:52",
			"flickr_url": "http://farm7.staticflickr.com/6116/6255196340_da26cf2c9e_z.jpg",
			"id": 397133
		},
		{
			"license": 1,
			"file_name": "000000158227.jpg",
			"coco_url": "http://images.cocodataset.org/val2017/000000158227.jpg",
			"height": 357,
			"width": 500,
			"date_captured": "2013-11-22 23:05:30",
			"flickr_url": "http://farm1.staticflickr.com/210/495201463_3d6b548c08_z.jpg",
			"id": 158227
		},
		{
			"license": 1,
			"file_name": "000000407646.jpg",
			"coco_url": "http://images.cocodataset.org/val2017/000000407646.jpg",
			"height": 400,
			"width": 500,
			"date_captured": "2013-11-23 03:58:53",
			"flickr_url": "http://farm4.staticflickr.com/3110/2855627782_17b93a684e_z.jpg",
			"id": 407646
		},
	],
	"annotations": [{
			"segmentation": [
				[510.66, 510.03, 423.01, 510.45, 423.01]
			],
			"area": 702.1057499999998,
			"iscrowd": 0,
			"image_id": 289343,
			"bbox": [473.07, 395.93, 38.65, 28.67],
			"category_id": 18,
			"id": 1768
		},
		{
			"segmentation": [
				[289.749, 228.64, 444.27, 291.88, 443.74]
			],
			"area": 27718.476299999995,
			"iscrowd": 0,
			"image_id": 61471,
			"bbox": [272.1, 200.23, 151.97, 279.77],
			"category_id": 18,
			"id": 1773
		},
		{
			"segmentation": {
				"counts": [272, 2, 4, , 221, 16, 228, 8, 10250],
				"size": [240, 320]
			},
			"area": 18419,
			"iscrowd": 1,
			"image_id": 448263,
			"bbox": [1, 0, 276, 122],
			"category_id": 1,
			"id": 900100448263
		},
		{
			"segmentation": {
				"counts": [8214, 6, 6634, 4, 99294],
				"size": [640, 549]
			},
			"area": 962,
			"iscrowd": 1,
			"image_id": 374545,
			"bbox": [12, 524, 381, 33],
			"category_id": 1,
			"id": 900100374545
		},
		{
			"segmentation": {
				"counts": [66916, 6, 58785, 8, 588, 1, 114303],
				"size": [594, 640]
			},
			"area": 6197,
			"iscrowd": 1,
			"image_id": 284445,
			"bbox": [112, 322, 335, 94],
			"category_id": 1,
			"id": 900100284445
		},
		{"supercategory": "person","id": 1,"name": "person"},
		{"supercategory": "vehicle","id": 2,"name": "bicycle"},
		{"supercategory": "vehicle","id": 3,"name": "car"},
		{"supercategory": "vehicle","id": 4,"name": "motorcycle"},
		{"supercategory": "vehicle","id": 5,"name": "airplane"},
		{"supercategory": "vehicle","id": 6,"name": "bus"},
		{"supercategory": "vehicle","id": 7,"name": "train"},
		{"supercategory": "vehicle","id": 8,"name": "truck"},
		{"supercategory": "vehicle","id": 9,"name": "boat"},
		{"supercategory": "outdoor","id": 10,"name": "traffic light"},
		{"supercategory": "outdoor","id": 11,"name": "fire hydrant"},
		{"supercategory": "outdoor","id": 13,"name": "stop sign"},
		{"supercategory": "outdoor","id": 14,"name": "parking meter"},
		{"supercategory": "outdoor","id": 15,"name": "bench"},
		{"supercategory": "animal","id": 16,"name": "bird"},
		{"supercategory": "animal","id": 17,"name": "cat"},
		{"supercategory": "animal","id": 18,"name": "dog"},
		{"supercategory": "animal","id": 19,"name": "horse"},
		{"supercategory": "animal","id": 20,"name": "sheep"},
		{"supercategory": "animal","id": 21,"name": "cow"},
		{"supercategory": "animal","id": 22,"name": "elephant"},
		{"supercategory": "animal","id": 23,"name": "bear"},
		{"supercategory": "animal","id": 24,"name": "zebra"},
		{"supercategory": "animal","id": 25,"name": "giraffe"},
		{"supercategory": "accessory","id": 27,"name": "backpack"},
		{"supercategory": "accessory","id": 28,"name": "umbrella"},
		{"supercategory": "accessory","id": 31,"name": "handbag"},
		{"supercategory": "accessory","id": 32,"name": "tie"},
		{"supercategory": "accessory","id": 33,"name": "suitcase"},
		{"supercategory": "sports","id": 34,"name": "frisbee"},
		{"supercategory": "sports","id": 35,"name": "skis"},
		{"supercategory": "sports","id": 36,"name": "snowboard"},
		{"supercategory": "sports","id": 37,"name": "sports ball"},
		{"supercategory": "sports","id": 38,"name": "kite"},
		{"supercategory": "sports","id": 39,"name": "baseball bat"},
		{"supercategory": "sports","id": 40,"name": "baseball glove"},
		{"supercategory": "sports","id": 41,"name": "skateboard"},
		{"supercategory": "sports","id": 42,"name": "surfboard"},
		{"supercategory": "sports","id": 43,"name": "tennis racket"},
		{"supercategory": "kitchen","id": 44,"name": "bottle"},
		{"supercategory": "kitchen","id": 46,"name": "wine glass"},
		{"supercategory": "kitchen","id": 47,"name": "cup"},
		{"supercategory": "kitchen","id": 48,"name": "fork"},
		{"supercategory": "kitchen","id": 49,"name": "knife"},
		{"supercategory": "kitchen","id": 50,"name": "spoon"},
		{"supercategory": "kitchen","id": 51,"name": "bowl"},
		{"supercategory": "food","id": 52,"name": "banana"},
		{"supercategory": "food","id": 53,"name": "apple"},
		{"supercategory": "food","id": 54,"name": "sandwich"},
		{"supercategory": "food","id": 55,"name": "orange"},
		{"supercategory": "food","id": 56,"name": "broccoli"},
		{"supercategory": "food","id": 57,"name": "carrot"},
		{"supercategory": "food","id": 58,"name": "hot dog"},
		{"supercategory": "food","id": 59,"name": "pizza"},
		{"supercategory": "food","id": 60,"name": "donut"},
		{"supercategory": "food","id": 61,"name": "cake"},
		{"supercategory": "furniture","id": 62,"name": "chair"},
		{"supercategory": "furniture","id": 63,"name": "couch"},
		{"supercategory": "furniture","id": 64,"name": "potted plant"},
		{"supercategory": "furniture","id": 65,"name": "bed"},
		{"supercategory": "furniture","id": 67,"name": "dining table"},
		{"supercategory": "furniture","id": 70,"name": "toilet"},
		{"supercategory": "electronic","id": 72,"name": "tv"},
		{"supercategory": "electronic","id": 73,"name": "laptop"},
		{"supercategory": "electronic","id": 74,"name": "mouse"},
		{"supercategory": "electronic","id": 75,"name": "remote"},
		{"supercategory": "electronic","id": 76,"name": "keyboard"},
		{"supercategory": "electronic","id": 77,"name": "cell phone"},
		{"supercategory": "appliance","id": 78,"name": "microwave"},
		{"supercategory": "appliance","id": 79,"name": "oven"},
		{"supercategory": "appliance","id": 80,"name": "toaster"},
		{"supercategory": "appliance","id": 81,"name": "sink"},
		{"supercategory": "appliance","id": 82,"name": "refrigerator"},
		{"supercategory": "indoor","id": 84,"name": "book"},
		{"supercategory": "indoor","id": 85,"name": "clock"},
		{"supercategory": "indoor","id": 86,"name": "vase"},
		{"supercategory": "indoor","id": 87,"name": "scissors"},
		{"supercategory": "indoor","id": 88,"name": "teddy bear"},
		{"supercategory": "indoor","id": 89,"name": "hair drier"},
		{"supercategory": "indoor","id": 90,"name": "toothbrush"}
	]
}

帮助文档

COCO数据集的标注格式

微信：宏沉一笑
公众号：漫步之行

签名：Smile every day
名字：宏沉一笑
邮箱：whghcyx@outlook.com
个人网站：https://whg555.github.io

转载请注明来源，欢迎对文章中的引用来源进行考证，欢迎指出任何有错误或不够清晰的表达。可以在下面评论区评论，也可以邮件至 whghcyx@outlook.com

赏

文章标题:COCO数据集

文章字数:3.3k

本文作者:宏沉一笑

发布时间:2020-02-04, 14:48:14

最后更新:2024-03-21, 12:53:35

原始链接:https://whghcyx.gitee.io/2020/02/04/AI-2020-2-4-COCO%E6%95%B0%E6%8D%AE%E9%9B%86/

版权声明: "署名-非商用-相同方式共享 4.0" 转载请保留原文链接及作者。

©2016-2019 Hcyx

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