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}
}