---
license: mit
pretty_name: PartImageNet++
size_categories:
- 100K<n<1M
---
## PartImageNet++ Dataset

PartImageNet++ is an extensive dataset designed for robust object recognition and segmentation tasks. This dataset expands upon the original ImageNet dataset by providing detailed part annotations for each object category.


### Dataset Statistics
| Obj. Cat. | Part Cat. | Img    | Part Mask |
| --------- | --------- | ------ | --------- |
| 1000      | 3308      | 100000 | 406364    |

The dataset includes:
- **1000 object categories** derived from the original ImageNet.
- **3308 part categories** representing different parts of objects.
- **100,000 annotated images**, with each object category containing 100 images.
- **406,364 part mask annotations** ensuring comprehensive coverage and detailed segmentation.

### Structure and Contents

Each JSON file in the `json` directory represents one object category and its corresponding part annotations. 

The `including` folder provides detailed inclusion relations of parts, illustrating hierarchical relationships between different part categories.

The `discarded_data.json` file lists low-quality images that were excluded from the dataset to maintain high annotation standards.

### Visualizations

We provide a visualization demo tool to explore and inspect the annotations. This tool helps users to better understand the structure and details of the dataset.

### If you find this useful in your research, please cite this work:
```
@inproceedings{li2024languagedriven,
  author = {Li, Xiao and Liu, Yining and Dong, Na and Sitian Qin and Hu, Xiaolin},
  title = {Language-Driven Anchors for Zero-Shot Adversarial Robustness},
  booktitle={European conference on computer vision},
  year = {2024},
  organization={Springer}
}
```