Skip to content

Latest commit

 

History

History
138 lines (94 loc) · 6.27 KB

README.md

File metadata and controls

138 lines (94 loc) · 6.27 KB
Raw

Logo

A detection/segmentation dataset with class names characterized by intricate and flexible expressions

The repo is the toolbox for D3
[Doc 📚] [Paper (DOD) 📄] [Paper (GRES) 📄] [Awesome-DOD 🕶️]

Description Detection Dataset ($D^3$, /dikju:b/) is an attempt at creating a next-generation object detection dataset. Unlike traditional detection datasets, the class names of the objects are no longer just simple nouns or noun phrases, but rather complex and descriptive descriptions, such as a dog not being held by a leash. For each image in the dataset, any object that matches the description is annotated. The dataset provides annotations such as bounding boxes and finely crafted instance masks. We believe it will contribute to computer vision and vision-language communities.

News

  • [02/14/2024] Evaluation on several SOTA methods (SPHNX (the first MLLM evaluated!), G-DINO, UNINEXT, etc.) are released, together with a leaderboard for $D^3$. 🔥🔥

  • [10/12/2023] We released an awesome-described-object-detection list to collect and track related works.

  • [09/22/2023] Our DOD paper just got accepted by NeurIPS 2023! 🔥

  • [07/25/2023] This toolkit is available on PyPI now. You can install this repo with pip install ddd-dataset.

  • [07/25/2023] The paper preprint introducing the DOD task and the $D^3$ dataset, is available on arxiv. Check it out!

  • [07/18/2023] We have released our Description Detection Dataset ($D^3$) and the first version of $D^3$ toolbox. You can download it now for your project.

  • [07/14/2023] Our GRES paper has been accepted by ICCV 2023.

Contents

Task and Dataset Highlight

The $D^3$ dataset is meant for the Described Object Detection (DOD) task. In the image below we show the difference between Referring Expression Comprehension (REC), Object Detection/Open-Vocabulary Detection (OVD) and Described Object Detection (DOD). OVD detect object based on category name, and each category can have zero to multiple instances; REC grounds one region based on a language description, whether the object truly exits or not; DOD detect all instances on each image in the dataset, based on a flexible reference. Related works are tracked in the awesome-DOD list.

Dataset Highlight

For more information on the characteristics of this dataset, please refer to our paper.

Download

Currently we host the $D^3$ dataset on cloud drives. You can download the dataset from Google Drive or Baidu Pan.

After downloading the d3_images.zip (images in the dataset), d3_pkl.zip (dataset information for this toolkit) and d3_json.zip (annotation for evaluation), please extract these 3 zip files to your custom IMG_ROOT, PKL_PATH and JSON_ANNO_PATH directory. These paths will be used when you perform inference or evaluation on this dataset.

Installation

Prerequisites

This toolkit requires a few python packages like numpy and pycocotools. Other packages like matplotlib and opencv-python may also be required if you want to utilize the visualization scripts.

There are multiple ways to install $D^3$ toolbox, as listed below:

Install with pip

pip install ddd-dataset

Install from source

git clone https://github.com/shikra/d-cube.git
# option 1: install it as a python package
cd d-cube
python -m pip install .
# done

# option 2: just put the d-cube/d_cube directory in the root directory of your local repository

Usage

Please refer to the documentation 📚 for more details. Our toolbox is similar to cocoapi in style.

Here is a quick example of how to use $D^3$.

from d_cube import D3
d3 = D3(IMG_ROOT, PKL_ANNO_PATH)
all_img_ids = d3.get_img_ids()  # get the image ids in the dataset
all_img_info = d3.load_imgs(all_img_ids)  # load images by passing a list of some image ids
img_path = all_img_info[0]["file_name"]  # obtain one image path so you can load it and inference

Some frequently asked questions are answered in this Q&A file.

Citation

If you use our $D^3$ dataset, this toolbox, or otherwise find our work valuable, please cite our paper:

@inproceedings{xie2023DOD,
  title={Described Object Detection: Liberating Object Detection with Flexible Expressions},
  author={Xie, Chi and Zhang, Zhao and Wu, Yixuan and Zhu, Feng and Zhao, Rui and Liang, Shuang},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)},
  year={2023}
}

@inproceedings{wu2023gres,
  title={Advancing Referring Expression Segmentation Beyond Single Image},
  author={Wu, Yixuan and Zhang, Zhao and Xie, Chi and Zhu, Feng and Zhao, Rui},
  booktitle={International Conference on Computer Vision (ICCV)},
  year={2023}
}

More works related to Described Object Detection are tracked in this list: awesome-described-object-detection.