Datasets:

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}
	}
	```