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.
Example Figures
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