manya-codes/coco2017

---
pretty_name: COCO2017
annotations_creators:
- expert-generated
size_categories:
- 100K<n<1M
language:
- en
task_categories:
- object-detection
---

# Dataset Card for Dataset Name

This dataset includes **COCO 2017** only.

COCO 2014 and 2015 will be included soon.

## Dataset Description

- **Homepage:** https://cocodataset.org/
- **Repository:** https://github.com/cocodataset/cocoapi
- **Paper:** [Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)

### Dataset Summary

COCO (Common Objects in Context) is a large-scale object detection, segmentation, and captioning dataset. It contains over 200,000 labeled images with over 80 category labels. It includes complex, everyday scenes with common objects in their natural context.

This dataset covers only the "object detection" part of the COCO dataset. But some features and specifications for the full COCO dataset:

- Object segmentation
- Recognition in context
- Superpixel stuff segmentation
- 330K images (>200K labeled)
- 1.5 million object instances
- 80 object categories
- 91 stuff categories
- 5 captions per image
- 250,000 people with keypoints

### Data Splits

- **Training set ("train")**: 118287 images annotated with 860001 bounding boxes in total.
- **Validation set ("val")**: 5000 images annotated with 36781 bounding boxes in total.

- **92 classes**: "None", "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light", "fire hydrant", "street sign", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "hat", "backpack", "umbrella", "shoe", "eye glasses", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "plate", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", "bed", "mirror", "dining table", "window", "desk", "toilet", "door", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator", "blender", "book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush", "hair brush"

- **But only 80 classes have with annotations**: "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"

### Boxes format:

For the object detection set of COCO dataset, the ground-truth bounding boxes are provided in the following format: `x, y, width, height` in absolute coordinates.

### Curation Rationale

COCO dataset was curated with the goal of advancing the state of the art in many tasks, such as object detection, dense pose, keypoints, segmentation and image classification.

### Licensing Information

The annotations in this dataset belong to the COCO Consortium and are licensed under a Creative Commons Attribution 4.0 License.

Mode details at: https://cocodataset.org/#termsofuse

### Loading dataset

You can load COCO 2017 dataset by calling:

```
from datasets import load_dataset
# Full dataset
dataset = load_dataset("rafaelpadilla/coco2017")
print(dataset)
>> DatasetDict({
>> train: Dataset({
>> features: ['image', 'image_id', 'objects'],
>> num_rows: 118287
>> })
>> val: Dataset({
>> features: ['image', 'image_id', 'objects'],
>> num_rows: 5000
>> })
>> })

# Training set only
dataset = load_dataset("rafaelpadilla/coco2017", split="train")

# Validation set only
dataset = load_dataset("rafaelpadilla/coco2017", split="val")
```

### COCODataset Class

We offer the dataset class `COCODataset` that extends VisionDataset to represents images and annotations of COCO. To use it, you need to install coco2017 package. For that, follow the steps below:

1. Create and activate an environment:
```
conda create -n coco2017 python=3.11
conda activate coco2017
```

2. Install cocodataset package:

```
pip install git+https://huggingface.co/datasets/rafaelpadilla/coco2017@main
```

or alternatively:

```
git clone https://huggingface.co/datasets/rafaelpadilla/coco2017
cd coco2017
pip install .
```

3. Now you can import `COCODataset` class into your Python code by:
```
from cocodataset import COCODataset
```

### Citation Information

@inproceedings{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 Doll{\'a}r, Piotr and Zitnick, C Lawrence},
booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
pages={740--755},
year={2014},
organization={Springer}
}

### Contributions

Tsung-Yi Lin Google Brain
Genevieve Patterson MSR, Trash TV
Matteo R. Ronchi Caltech
Yin Cui Google
Michael Maire TTI-Chicago
Serge Belongie Cornell Tech
Lubomir Bourdev WaveOne, Inc.
Ross Girshick FAIR
James Hays Georgia Tech
Pietro Perona Caltech
Deva Ramanan CMU
Larry Zitnick FAIR
Piotr Dollár FAIR

数据集名称：COCO2017
标注生成方式：专家标注
样本量区间：10万 < 样本数 < 100万
语言：英语
任务类别：目标检测（object-detection）

# 数据集卡片：COCO2017

本数据集仅包含**COCO 2017**版本，COCO 2014与2015版本将在后续更新中加入。

## 数据集说明

- **主页**：https://cocodataset.org/
- **代码仓库**：https://github.com/cocodataset/cocoapi
- **相关论文**：[Microsoft COCO: Common Objects in Context](https://arxiv.org/abs/1405.0312)

### 数据集概述

COCO（Common Objects in Context，通用上下文物体）是一个大规模的目标检测、图像分割与图像描述数据集，包含超过20万张标注图像，对应80余个类别标签，涵盖日常场景中常见物体的自然上下文复杂场景。

本数据集仅涵盖COCO数据集的目标检测分支，完整COCO数据集的部分特性与规格如下：
- 目标分割
- 上下文识别
- 超像素素材分割
- 33万张图像（其中超过20万张带有标注）
- 150万个物体实例
- 80个物体类别
- 91个素材类别
- 每张图像对应5条图像描述
- 25万张带有关键点的人物图像

### 数据划分

- **训练集（"train"）**：共118287张图像，总计标注有860001个边界框。
- **验证集（"val"）**：共5000张图像，总计标注有36781个边界框。

- **共92个类别**："person（人物）", "bicycle（自行车）", "car（轿车）", "motorcycle（摩托车）", "airplane（飞机）", "bus（公共汽车）", "train（火车）", "truck（卡车）", "boat（船只）", "traffic light（交通信号灯）", "fire hydrant（消防栓）", "street sign（街道标识）", "stop sign（停车让行标志）", "parking meter（停车计时器）", "bench（长椅）", "bird（鸟类）", "cat（猫）", "dog（狗）", "horse（马）", "sheep（绵羊）", "cow（奶牛）", "elephant（大象）", "bear（熊）", "zebra（斑马）", "giraffe（长颈鹿）", "hat（帽子）", "backpack（背包）", "umbrella（雨伞）", "shoe（鞋子）", "eye glasses（眼镜）", "handbag（手提包）", "tie（领带）", "suitcase（行李箱）", "frisbee（飞盘）", "skis（滑雪板）", "snowboard（单板滑雪板）", "sports ball（运动球）", "kite（风筝）", "baseball bat（棒球棒）", "baseball glove（棒球手套）", "skateboard（滑板）", "surfboard（冲浪板）", "tennis racket（网球拍）", "bottle（饮料瓶）", "plate（盘子）", "wine glass（葡萄酒杯）", "cup（杯子）", "fork（叉子）", "knife（餐刀）", "spoon（勺子）", "bowl（碗）", "banana（香蕉）", "apple（苹果）", "sandwich（三明治）", "orange（橙子）", "broccoli（西兰花）", "carrot（胡萝卜）", "hot dog（热狗）", "pizza（披萨）", "donut（甜甜圈）", "cake（蛋糕）", "chair（椅子）", "couch（长沙发）", "potted plant（盆栽）", "bed（床）", "mirror（镜子）", "dining table（餐桌）", "window（窗户）", "desk（书桌）", "toilet（马桶）", "door（门）", "tv（电视机）", "laptop（笔记本电脑）", "mouse（鼠标）", "remote（遥控器）", "keyboard（键盘）", "cell phone（手机）", "microwave（微波炉）", "oven（烤箱）", "toaster（烤面包机）", "sink（水槽）", "refrigerator（冰箱）", "blender（搅拌机）", "book（书籍）", "clock（时钟）", "vase（花瓶）", "scissors（剪刀）", "teddy bear（泰迪熊）", "hair drier（吹风机）", "toothbrush（牙刷）", "hair brush（发刷）"

- **仅80个类别带有标注**："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"

### 边界框格式

针对COCO数据集的目标检测分支，其真实边界框采用以下格式：以绝对坐标表示的`x, y, width, height`（左上角横坐标、左上角纵坐标、框宽、框高）。

### 数据集构建初衷

COCO数据集的构建目标是推动多项计算机视觉任务的技术前沿进展，涵盖目标检测、密集姿态估计、关键点检测、图像分割与图像分类等任务。

### 授权信息

本数据集的标注内容归属于COCO联盟，采用知识共享署名4.0（Creative Commons Attribution 4.0）协议进行授权。更多详情请访问：https://cocodataset.org/#termsofuse

### 数据集加载方式

你可以通过以下代码加载COCO 2017数据集：
python
from datasets import load_dataset
# 加载完整数据集
dataset = load_dataset("rafaelpadilla/coco2017")
print(dataset)
>> DatasetDict({
>> train: Dataset({
>> features: ['image', 'image_id', 'objects'],
>> num_rows: 118287
>> })
>> val: Dataset({
>> features: ['image', 'image_id', 'objects'],
>> num_rows: 5000
>> })
>> })

# 仅加载训练集
dataset = load_dataset("rafaelpadilla/coco2017", split="train")

# 仅加载验证集
dataset = load_dataset("rafaelpadilla/coco2017", split="val")

### COCODataset类

我们提供了继承自VisionDataset的`COCODataset`类，用于表示COCO数据集的图像与标注信息。如需使用该类，请先安装coco2017工具包，具体步骤如下：

1. 创建并激活虚拟环境：

conda create -n coco2017 python=3.11
conda activate coco2017

2. 安装cocodataset包：

pip install git+https://huggingface.co/datasets/rafaelpadilla/coco2017@main

或者通过以下方式安装：

git clone https://huggingface.co/datasets/rafaelpadilla/coco2017
cd coco2017
pip install .

3. 在Python代码中导入`COCODataset`类：
python
from cocodataset import COCODataset

### 引用信息

请引用以下论文以使用本数据集：
bibtex
@inproceedings{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, C Lawrence},
booktitle={Computer Vision--ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13},
pages={740--755},
year={2014},
organization={Springer}
}

### 贡献者

本数据集由以下人员共同贡献：
Tsung-Yi Lin Google Brain
Genevieve Patterson MSR, Trash TV
Matteo R. Ronchi Caltech
Yin Cui Google
Michael Maire TTI-Chicago
Serge Belongie Cornell Tech
Lubomir Bourdev WaveOne, Inc.
Ross Girshick FAIR
James Hays Georgia Tech
Pietro Perona Caltech
Deva Ramanan CMU
Larry Zitnick FAIR
Piotr Dollár FAIR