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---
license: mit
pretty_name: PartImageNet++
viewer: false
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.
Official repository for [PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition](https://huggingface.co./papers/2407.10918).
### Dataset Statistics
The dataset includes:
- **1000 object categories** derived from the original ImageNet-1K.
- **3308 part categories** representing different parts of objects.
- **100,000 annotated images**, with each object category containing 100 images (downloaded from the ImageNet-1K dataset).
- **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 excluded from the dataset to maintain high annotation standards.
The category_name.json file contains each JSON file's file name, along with its corresponding part name and object name.
### Visualizations
We provide a visualization demo tool to explore and inspect the annotations. This tool helps users better understand the dataset's structure and details.
### If you find this useful in your research, please cite this work:
```
@inproceedings{li2024pinpp,
author = {Li, Xiao and Liu, Yining and Dong, Na and Qin, Sitian and Hu, Xiaolin},
title = {PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition},
booktitle={European conference on computer vision},
year = {2024},
organization={Springer}
}
```
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