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---
license: mit
task_categories:
- image-classification
- image-segmentation
size_categories:
- 100K<n<1M
pretty_name: PartImageNet++
viewer: false
---
## PartImageNet++ Dataset
[Paper](https://huggingface.co/papers/2601.01454) | [GitHub](https://github.com/LixiaoTHU/PartImageNetPP)
PartImageNet++ (PIN++) 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/2601.01454).
### 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.
### Usage
Train a model, e.g., an MPM with ResNet-50-GELU as the backbone by AT:
```bash
python -m torch.distributed.launch --nproc_per_node=8 adv_train.py --configs=./train_configs/1k_pp/resnet50_part_pp_init.yaml
python -m torch.distributed.launch --nproc_per_node=8 adv_train.py --configs=./train_configs/1k_pp/resnet50_part_pp_at.yaml
```
Evaluate the trained models with Autoattack under different attack threats, e.g., the MPM model under L2 attack with $\epsilon = 2$ :
```bash
python eval.py \
--configs=./train_configs/1k_pp/resnet50_part_pp_at.yaml \
--checkpoint=/home/user/ssd/*/checkpoint.pth \
--attack_types="autoattack" \
--norm "L2" \
--eval-eps 2
```
### 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.
### Citation
If you find this useful in your research, please cite this work:
```bibtex
@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}
}
``` |