---
license: mit
task_categories:
- image-segmentation
tags:
- wsss
- boundary-refinement
- priors
---

# PASCAL VOC 2012 NAMLab Priors for HGA

This dataset repository hosts the pre-computed hierarchical invariant region partition priors for weakly supervised semantic segmentation (WSSS), generated using the NAMLab framework.

These priors serve as the 2D structural guidance stream for the **HGA (Hierarchical-Geometric Alignment)** boundary internalization paradigm.

## 🔗 Main Codebase
The complete implementation, environment setup, and training guidelines are available in our official GitHub repository:
👉 **[Uncertainty-42/HGA](https://github.com/Uncertainty-42/HGA)**

## 📂 Expected Directory Layout
Download the `priors_voc.zip` archive (approx. 4.2 GB) and extract its contents into your local PASCAL VOC dataset structure as shown below:

```text
VOCdevkit/VOC2012/
├── JPEGImages/                # Original source images (.jpg)
├── SegmentationClassAug/      # Ground Truth masks (.png)
└── priors/                    
    └── namlab_pt/             # <- Extract zip contents directly here (.pt files)
```

## 📥 Extraction Command
Navigate to your local `priors/` directory and run:
```bash
unzip priors_voc.zip -d namlab_pt/
```

## 📝 Citation & Attribution
If you use these priors in your research, please cite both our work and the foundational NAMLab paper:

```bibtex
@article{zheng2021hierarchical,
  title={Hierarchical Image Segmentation Based on Nonsymmetry and Anti-Packing Pattern Representation Model},
  author={Zheng, Yunping and Yang, Bowen and Sarem, Mudar},
  journal={IEEE Transactions on Image Processing},
  volume={30},
  pages={2408--2421},
  year={2021},
  publisher={IEEE}
}
```