Update README.md

README.md

@@ -1,3 +1,60 @@
 ---
 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}
+}
+```