| --- |
| license: mit |
| task_categories: |
| - image-segmentation |
| tags: |
| - wsss |
| - boundary-refinement |
| - priors |
| --- |
| |
| # MS COCO 2014 NAMLab Priors for HGA |
|
|
| This dataset repository hosts the pre-computed hierarchical invariant region partition priors for weakly supervised semantic segmentation (WSSS) on MS COCO 2014, generated using the NAMLab framework. |
|
|
| Due to Hugging Face's individual file size limits, the total ~63.69 GB dataset is uploaded as a **split-volume zip archive** (`priors_coco.zip.part1` to `priors_coco.zip.part3`). You must concatenate them locally before extraction. |
|
|
| ## 🔗 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 |
| After concatenation and extraction, the folder structure should match the following layout: |
|
|
| ```text |
| MSCOCO/ |
| ├── JPEGImages/ |
| │ ├── train/ |
| │ └── val/ |
| └── priors/ |
| └── namlab_pt/ # <- Extract here (will automatically create train/ and val/) |
| ``` |
|
|
| ## 📥 Concatenation & Extraction Guide |
| Navigate to your local `priors/` directory where the downloaded split parts reside, and run the following commands to reconstruct and extract the dataset: |
|
|
| ```bash |
| # Step 1: Concatenate the split parts into a single valid zip archive |
| cat priors_coco.zip.part* > priors_coco.zip |
| |
| # Step 2: Extract the contents into namlab_pt/ |
| unzip priors_coco.zip -d namlab_pt/ |
| |
| # Step 3: Clean up the temporary archive and parts (Optional) |
| rm priors_coco.zip.part* priors_coco.zip |
| ``` |
|
|
| ## 📝 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} |
| } |
| ``` |
