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PartImageNet: A Large, High-Quality Dataset of Parts

UPDATE

We offer two splits for the PartImageNet dataset now.

The dataset is ready!

Our annotations strictly follow the coco style so it should be easy to use the cocoapi for visulization the images and annotations.

If you find our work helpful in your research, please cite it as:

@article{he2021partimagenet,
  title={PartImageNet: A Large, High-Quality Dataset of Parts},
  author={He, Ju and Yang, Shuo and Yang, Shaokang and Kortylewski, Adam and Yuan, Xiaoding and Chen, Jie-Neng and Liu, Shuai and Yang, Cheng and Yuille, Alan},
  journal={arXiv preprint arXiv:2112.00933},
  year={2021}
}

Introduction

PartImageNet is a large, high-quality dataset with part segmentation annotations. It consists of 158 classes from ImageNet with approximately 24′000 images. The classes are grouped into 11 super-categories and the parts split are designed according to the super-category as shown below. The number in the brackets after the category name indicates the total number of classes of the category.

CategoryAnnotated Parts
Quadruped (46)Head, Body, Foot, Tail
Biped (17)Head, Body, Hand, Foot, Tail
Fish (10)Head, Body, Fin, Tail
Bird (14)Head, Body, Wing, Foot, Tail
Snake (15)Head, Body
Reptile (20)Head, Body, Foot, Tail
Car (19)Body, Tier, Side Mirror
Bicycle (6)Head, Body, Seat, Tier
Boat (4)Body, Sail
Aeroplane (2)Head, Body, Wing, Engine, Tail
Bottle (5)Body, Mouth

The statistics of train/val/test split is shown below.

SplitNumber of classesNumber of images
Train10916540
Val192957
Test304598
Total15824095

For more detailed statistics, please check out our paper.

Possible Usage

PartImageNet has broad potential in and can be benefit to numerious research fields while we simply explore its usage in Part Discovery, Few-shot Learning and Semantic Segmentation in the paper. We hope that with the propose of the PartImageNet, we could attarct more attention to the part-based models and yield more interesting works.

Example Figures