| --- |
| license: mit |
| pretty_name: PartImageNet++ |
| size_categories: |
| - 100K<n<1M |
| --- |
| ## PartImageNet++ Dataset |
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| 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. |
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| ### Dataset Statistics |
| | Obj. Cat. | Part Cat. | Img | Part Mask | |
| | --------- | --------- | ------ | --------- | |
| | 1000 | 3308 | 100000 | 406364 | |
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| 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. |
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| ### Structure and Contents |
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| Each JSON file in the `json` directory represents one object category and its corresponding part annotations. |
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| The `including` folder provides detailed inclusion relations of parts, illustrating hierarchical relationships between different part categories. |
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| The `discarded_data.json` file lists low-quality images that were excluded from the dataset to maintain high annotation standards. |
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| ### Visualizations |
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| 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. |
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| ### 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} |
| } |
| ``` |
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