Francesco/aerial-spheres

---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': aerial-spheres
'1': green_sphero
'2': orange-sphero
'3': orange_sphero
'4': purple_sphero
'5': red_sphero
'6': yellow_sphero
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: aerial-spheres
tags:
- rf100
---

# Dataset Card for aerial-spheres

** The original COCO dataset is stored at `dataset.tar.gz`**

## Dataset Description

- **Homepage:** https://universe.roboflow.com/object-detection/aerial-spheres
- **Point of Contact:** [e-mail]

### Dataset Summary

aerial-spheres

### Supported Tasks and Leaderboards

- `object-detection`: The dataset can be used to train a model for Object Detection.

### Languages

English

## Dataset Structure

### Data Instances

A data point comprises an image and its object annotations.

```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```

### Data Fields

- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.

#### Who are the annotators?

Annotators are Roboflow users

## Additional Information

### Licensing Information

See original homepage https://universe.roboflow.com/object-detection/aerial-spheres

### Citation Information

```
@misc{ aerial-spheres,
title = { aerial spheres Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/aerial-spheres } },
url = { https://universe.roboflow.com/object-detection/aerial-spheres },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```

### Contributions

Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset.

原始信息汇总

数据集概述

名称: aerial-spheres

任务类别: 对象检测

语言: 英语

数据集大小: 1K<n<10K

数据集结构

数据实例

每个数据点包含以下信息:

• image_id: 整数，图像的唯一标识。
• image: 图像文件，类型为PIL.Image.Image。
• width: 整数，图像的宽度。
• height: 整数，图像的高度。
• objects: 字典，包含图像中对象的元数据。
  • id: 整数序列，对象的标识。
  • area: 整数序列，对象的边界框面积。
  • bbox: 浮点数序列，对象的边界框坐标，格式为[x, y, width, height]。
  • category: 整数序列，对象的类别，对应类别名如下:
    • 0: aerial-spheres
    • 1: green_sphero
    • 2: orange-sphero
    • 3: orange_sphero
    • 4: purple_sphero
    • 5: red_sphero
    • 6: yellow_sphero

数据字段

• image_id: 图像的唯一标识符，整数类型。
• image: 图像文件，PIL.Image.Image类型。
• width: 图像的宽度，整数类型。
• height: 图像的高度，整数类型。
• objects: 包含对象元数据的字典:
  • id: 对象标识，整数序列。
  • area: 边界框面积，整数序列。
  • bbox: 边界框坐标，浮点数序列，长度为4。
  • category: 对象类别，类别名通过整数索引。