Buckets:
| 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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