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--- |
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license: mit |
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pretty_name: PartImageNet++ |
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size_categories: |
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- 100K<n<1M |
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--- |
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## 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 |
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The dataset includes: |
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- **1000 object categories** derived from the original ImageNet-1K. |
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- **3308 part categories** representing different parts of objects. |
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- **100,000 annotated images**, with each object category containing 100 images (downloaded from the ImageNet-1K dataset). |
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- **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 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 better understand the dataset's structure and details. |
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### If you find this useful in your research, please cite this work: |
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``` |
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@inproceedings{li2024pinpp, |
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author = {Li, Xiao and Liu, Yining and Dong, Na and Qin, Sitian and Hu, Xiaolin}, |
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title = {PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition}, |
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booktitle={European conference on computer vision}, |
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year = {2024}, |
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organization={Springer} |
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} |
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``` |
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