# V3Det: Vast Vocabulary Visual Detection Dataset
Jiaqi Wang*, Pan Zhang*, Tao Chu*, Yuhang Cao*,
Yujie Zhou, Tong Wu, Bin Wang, Conghui He, Dahua Lin
(* equal contribution)
Accepted to ICCV 2023 (Oral)

Paper, Dataset

## Codebase ### Object Detection - [x] mmdetection: https://github.com/V3Det/mmdetection-V3Det/tree/main/configs/v3det - [x] Detectron2： https://github.com/V3Det/Detectron2-V3Det ### Open Vocabulary Detection (OVD) - [x] 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/ / |────.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} } ```