## 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.


language: 
- English
pretty_name: "Pretty Name of the Dataset"
license: "MIT"
task_categories:
- object recognition
- part segmentation