PartImageNet: A Large, High-Quality Dataset of Parts

We will release our dataset and scripts soon after cleaning and approval.

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.

Category	Annotated 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 (23)	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.

Split	Number of classes	Number of images
Train	109	16540
Val	19	2957
Test	30	4598
Total	158	24095

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. We will release our implementation later as well.

PartImageNet is a large, high-quality dataset with part segmentation annotations

Related tags

Overview

PartImageNet: A Large, High-Quality Dataset of Parts

Introduction

Possible Usage

Example Figures

Owner

Ju He

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