---
title: COCO_2014
canonical_url: "https://www.modelscope.cn/datasets/OmniData/COCO_2014"
md_url: "https://www.modelscope.cn/datasets/OmniData/COCO_2014.md"
repository: OmniData/COCO_2014
last_updated: 2024-07-14
license: "[CC BY 4.0]"
storage_size: "25 GB"
domain:
  - publishDate
  - displayName
  - publishUrl
  - mediaTypes
  - publisher
  - taskTypes
  - labelTypes
tasks:
  - 2014
  - "COCO 2014"
  - "https://cocodataset.org/"
  - Image
  - Microsoft
  - "Object Detection"
  - Text
downloads: 67422
stars: 4
---

# COCO_2014

> COCO_2014 - OmniData 在 ModelScope 开源的数据集。displayName: COCO 2014 labelTypes: Box2D Polygon Keypoints2D RLEPolygon Text license: CC BY 4.0 mediaTypes: Image paperUrl: "" publishDate: "2014" publishUrl: https://cocodataset.org/ publisher: Microsoft tags: Human…

OmniData/COCO_2014 是 ModelScope 魔搭社区上的2014、COCO 2014、https://cocodataset.org/数据集，涉及 publishDate、displayName、publishUrl 领域，存储大小 25 GB，采用 [CC BY 4.0] 许可。

- **Repository**: OmniData/COCO_2014
- **License**: [CC BY 4.0]
- **Tasks**: 2014, COCO 2014, https://cocodataset.org/, Image, Microsoft, Object Detection, Text
- **Domain**: publishDate, displayName, publishUrl, mediaTypes, publisher, taskTypes, labelTypes
- **Storage size**: 25 GB
- **Downloads**: 67422
- **Stars**: 4
- **Last updated**: 2024-07-14

Source: https://www.modelscope.cn/datasets/OmniData/COCO_2014

---

displayName: COCO 2014
labelTypes:
- Box2D
- Polygon
- Keypoints2D
- RLEPolygon
- Text
license:
- CC BY 4.0
mediaTypes:
- Image
paperUrl: ""
publishDate: "2014"
publishUrl: https://cocodataset.org/
publisher:
- Microsoft
tags:
- Human
taskTypes:
- Keypoint Detection
- Image Caption
- Object Detection

---
# 数据集介绍
  ## 简介
  COCO 是一个大规模的对象检测、分割和字幕数据集。 COCO有几个特点：
对象分割
上下文中的识别
超像素东西分割
330K 图像（>200K 标记）
150 万个对象实例
80 个对象类别
91个东西类别
每张图片 5 个字幕
250,000 人拥有关键点
  ## 类定义
  ```
instances:
person(person)
bicycle(vehicle)
car(vehicle)
motorcycle(vehicle)
airplane(vehicle)
bus(vehicle)
train(vehicle)
truck(vehicle)
boat(vehicle)
traffic light(outdoor)
fire hydrant(outdoor)
stop sign(outdoor)
parking meter(outdoor)
bench(outdoor)
bird(animal)
cat(animal)
dog(animal)
horse(animal)
sheep(animal)
cow(animal)
elephant(animal)
bear(animal)
zebra(animal)
giraffe(animal)
backpack(accessory)
umbrella(accessory)
handbag(accessory)
tie(accessory)
suitcase(accessory)
frisbee(sports)
skis(sports)
snowboard(sports)
sports ball(sports)
kite(sports)
baseball bat(sports)
baseball glove(sports)
skateboard(sports)
surfboard(sports)
tennis racket(sports)
bottle(kitchen)
wine glass(kitchen)
cup(kitchen)
fork(kitchen)
knife(kitchen)
spoon(kitchen)
bowl(kitchen)
banana(food)
apple(food)
sandwich(food)
orange(food)
broccoli(food)
carrot(food)
hot dog(food)
pizza(food)
donut(food)
cake(food)
chair(furniture)
couch(furniture)
potted plant(furniture)
bed(furniture)
dining table(furniture)
toilet(furniture)
tv(electronic)
laptop(electronic)
mouse(electronic)
remote(electronic)
keyboard(electronic)
cell phone(electronic)
microwave(appliance)
oven(appliance)
toaster(appliance)
sink(appliance)
refrigerator(appliance)
book(indoor)
clock(indoor)
vase(indoor)
scissors(indoor)
teddy bear(indoor)
hair drier(indoor)
toothbrush(indoor)

keypoints:
person(person)
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
```
  ## 引文
  ```
@article{lin2014microsoft,
        title={Microsoft Coco: Common Objects In Context},
        author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Dollár, Piotr and Zitnick, Lawrence C.},
        journal={COMPUTER VISION - ECCV 2014, PT V},
        pages={740--755},
        year={2014}
}
```
  
## Download dataset
:modelscope-code[]{type="git"}