myownskyW7/V3Det

---
license: cc-by-4.0
task_categories:
- object-detection
size_categories:
- 1K<n<10K
---

<p align="center">
<img src="images/v3det_icon.jpg" width="100"/>
</p>

<p align="center">
<b><font size="6">V3Det: Vast Vocabulary Visual Detection Dataset</font></b>
</p>

<p>
<div align="center">

<div>
<a href='https://myownskyw7.github.io/' target='_blank'>Jiaqi Wang</a>*,
<a href='https://panzhang0212.github.io/' target='_blank'>Pan Zhang</a>*,
Tao Chu*,
Yuhang Cao*, </br>
Yujie Zhou,
<a href='https://wutong16.github.io/' target='_blank'>Tong Wu</a>,
Bin Wang,
Conghui He,
<a href='http://dahua.site/' target='_blank'>Dahua Lin</a></br>
(* equal contribution)</br>
<strong>Accepted to ICCV 2023 (Oral)</strong>
</div>
</p>
<p>
<div>
<strong>
<a href='https://arxiv.org/pdf/2304.03752.pdf' target='_blank'>Paper</a>,
<a href='https://v3det.openxlab.org.cn/' target='_blank'>Dataset</a></br>
</strong>
</div>
</div>
</p>

<p align="center">
<img width=960 src="images/introduction.jpg"/>
</p>

## Codebase
### Object Detection
- mmdetection: https://github.com/V3Det/mmdetection-V3Det/tree/main/configs/v3det
- Detectron2： https://github.com/V3Det/Detectron2-V3Det

### Open Vocabulary Detection (OVD)
- Detectron2： https://github.com/V3Det/Detectron2-V3Det

## Data Format

The data includes a training set, a validation set, comprising 13,204 categories. The training set consists of 183,354 images, while the validation set has 29,821 images. The data organization is:
```
V3Det/
images/
<category_node>/
|────<image_name>.png
...
...
annotations/
|────v3det_2023_v1_category_tree.json # Category tree
|────category_name_13204_v3det_2023_v1.txt # Category name
|────v3det_2023_v1_train.json # Train set
|────v3det_2023_v1_val.json # Validation set
```

## Annotation Files

### Train/Val
The annotation files are provided in dictionary format and contain the keywords "images," "categories," and "annotations."

- images : store a list containing image information, where each element is a dictionary representing an image.
```
file_name # The relative image path, eg. images/n07745046/21_371_29405651261_633d076053_c.jpg.
height # The height of the image
width # The width of the image
id # Unique identifier of the image.
```

- categories : store a list containing category information, where each element is a dictionary representing a category.
```
name # English name of the category.
name_zh # Chinese name of the category.
cat_info # The format for the description information of categories is a list.
cat_info_gpt # The format for the description information of categories generated by ChatGPT is a list.
novel # For open-vocabulary detection, indicate whether the current category belongs to the 'novel' category.
id # Unique identifier of the category.
```

- annotations : store a list containing annotation information, where each element is a dictionary representing a bounding box annotation.
```
image_id # The unique identifier of the image where the bounding box is located.
category_id # The unique identifier of the category corresponding to the bounding box.
bbox # The coordinates of the bounding box, in the format [x, y, w, h], representing the top-left corner coordinates and the width and height of the box.
iscrowd # Whether the bounding box is a crowd box.
area # The area of the bounding box
```

### Category Tree
- The category tree stores information about dataset category mappings and relationships in dictionary format.
```
categoryid2treeid # Unique identifier of node in the category tree corresponding to the category identifier in dataset
id2name # English name corresponding to each node in the category tree
id2name_zh # Chinese name corresponding to each node in the category tree
id2desc # English description corresponding to each node in the category tree
id2desc_zh # Chinese description corresponding to each node in the category tree
id2synonym_list # List of synonyms corresponding to each node in the category tree
id2center_synonym # Center synonym corresponding to each node in the category tree
father2child # All direct child categories corresponding to each node in the category tree
child2father # All direct parent categories corresponding to each node in the category tree
ancestor2descendant # All descendant nodes corresponding to each node in the category tree
descendant2ancestor # All ancestor nodes corresponding to each node in the category tree
```

## Image Download

- Run the command to crawl the images. By default, the images will be stored in the './V3Det/' directory.
```
python v3det_image_download.py
```
- If you want to change the storage location, you can specify the desired folder by adding the option '--output_folder' when executing the script.
```
python v3det_image_download.py --output_folder our_folder
```

## Category Tree Visualization

- Run the command and then select dataset path `path/to/V3Det` to visualize the category tree.
```
python v3det_visualize_tree.py
```

Please refer to the [TreeUI Operation Guide](VisualTree.md) for more information.

## License:
- **V3Det Images**: Around 90% images in V3Det were selected from the [Bamboo Dataset](https://github.com/ZhangYuanhan-AI/Bamboo), sourced from the Flickr website. The remaining 10% were directly crawled from the Flickr. **We do not own the copyright of the images.** Use of the images must abide by the [Flickr Terms of Use](https://www.flickr.com/creativecommons/). We only provide lists of image URLs without redistribution.

- **V3Det Annotations**: The V3Det annotations, the category relationship tree, and related tools are licensed under a [Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/) (allow commercial use).

## Citation

```bibtex
@inproceedings{wang2023v3det,
title = {V3Det: Vast Vocabulary Visual Detection Dataset},
author = {Wang, Jiaqi and Zhang, Pan and Chu, Tao and Cao, Yuhang and Zhou, Yujie and Wu, Tong and Wang, Bin and He, Conghui and Lin, Dahua},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2023}
}
```

原始信息汇总

数据集概述

名称: V3Det: Vast Vocabulary Visual Detection Dataset

任务类别:

• 目标检测

数据规模:

• 1K<n<10K

数据格式:

• 包含训练集和验证集，共13,204个类别。
• 训练集包含183,354张图像，验证集包含29,821张图像。

数据组织:

V3Det/ images/ <category_node>/ |────<image_name>.png ... ... annotations/ |────v3det_2023_v1_category_tree.json # 类别树 |────category_name_13204_v3det_2023_v1.txt # 类别名称 |────v3det_2023_v1_train.json # 训练集 |────v3det_2023_v1_val.json # 验证集

标注文件:

• 训练/验证集标注文件以字典格式提供，包含关键字 "images"、"categories" 和 "annotations"。

  • images: 存储图像信息的列表，每个元素是一个表示图像的字典。

    file_name # 图像相对路径 height # 图像高度 width # 图像宽度 id # 图像唯一标识符

  • categories: 存储类别信息的列表，每个元素是一个表示类别的字典。

    name # 类别英文名称 name_zh # 类别中文名称 cat_info # 类别描述信息格式为列表 cat_info_gpt # ChatGPT生成的类别描述信息格式为列表 novel # 对于开放词汇检测，指示当前类别是否属于novel类别 id # 类别唯一标识符

  • annotations: 存储标注信息的列表，每个元素是一个表示边界框标注的字典。

    image_id # 边界框所在图像的唯一标识符 category_id # 边界框对应类别的唯一标识符 bbox # 边界框坐标，格式为[x, y, w, h]，表示左上角坐标和框的宽度及高度 iscrowd # 是否为人群框 area # 边界框面积

类别树:

• 类别树以字典格式存储数据集类别映射和关系信息。

  categoryid2treeid # 数据集中类别标识符对应的类别树节点唯一标识符 id2name # 类别树中每个节点的英文名称 id2name_zh # 类别树中每个节点的中文名称 id2desc # 类别树中每个节点的英文描述 id2desc_zh # 类别树中每个节点的中文描述 id2synonym_list # 类别树中每个节点的同义词列表 id2center_synonym # 类别树中每个节点的中心同义词 father2child # 类别树中每个节点的所有直接子类别 child2father # 类别树中每个节点的所有直接父类别 ancestor2descendant # 类别树中每个节点的所有后代节点 descendant2ancestor # 类别树中每个节点的所有祖先节点

许可证:

• V3Det Images: 约90%的图像来自Bamboo数据集，源自Flickr网站，其余10%直接从Flickr爬取。图像版权不属于我们，使用图像需遵守Flickr使用条款。
• V3Det Annotations: V3Det标注、类别关系树及相关工具采用Creative Commons Attribution 4.0 License（允许商业使用）。

搜集汇总

背景与挑战

背景概述

V3Det是一个大规模视觉检测数据集，包含13,204个类别和超过21万张图像，主要用于目标检测和开放词汇检测任务。数据集提供详细的类别树结构和丰富的标注信息，图像主要来自Flickr，标注采用CC-BY-4.0许可。