Francesco/avatar-recognition-nuexe

---
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': avatar
'1': Character
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: avatar-recognition-nuexe
tags:
- rf100
---

# Dataset Card for avatar-recognition-nuexe

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

## Dataset Description

- **Homepage:** https://universe.roboflow.com/object-detection/avatar-recognition-nuexe
- **Point of Contact:** [e-mail]

### Dataset Summary

avatar-recognition-nuexe

### 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/avatar-recognition-nuexe

### Citation Information

```
@misc{ avatar-recognition-nuexe,
title = { avatar recognition nuexe Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/avatar-recognition-nuexe } },
url = { https://universe.roboflow.com/object-detection/avatar-recognition-nuexe },
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

数据集基本信息

• 名称: avatar-recognition-nuexe
• 任务类型: 对象检测 (object-detection)
• 语言: 英语 (en)
• 许可证: 知识共享 (cc)
• 多语言性: 单语种 (monolingual)
• 数据集大小: 1K<n<10K
• 数据源: 原始数据 (original)

数据集结构

数据特征

• image_id: 整数类型 (int64)
• image: 图像类型
• width: 整数类型 (int32)
• height: 整数类型 (int32)
• objects: 序列类型，包含以下子特征:
  • id: 整数类型 (int64)
  • area: 整数类型 (int64)
  • bbox: 序列类型，长度为4的浮点数 (float32)
  • category: 分类标签，包含以下名称:
    • 0: avatar
    • 1: Character

数据实例

每个数据点包含以下信息:

• image_id: 图像的唯一标识
• image: 图像文件，为 PIL.Image.Image 对象
• width: 图像宽度
• height: 图像高度
• objects: 包含图像中对象的元数据，具体包括:
  • id: 对象的标识
  • area: 对象的面积
  • bbox: 对象的边界框坐标
  • category: 对象的类别

注释者

注释者为 Roboflow 用户。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于目标检测任务的小型图像数据集，包含314张统一尺寸（640x640像素）的图像，每张图像均标注有对象边界框和类别信息。数据集分为训练集（225张）、验证集（30张）和测试集（59张），适用于训练和评估目标检测模型，数据来源于Roboflow 100项目。