Update README.md

README.md

@@ -29,3 +29,106 @@ configs:
   - split: test
     path: data/test-*
 ---
+
+
+```markdown
+---
+license: cc-by-nc-4.0
+task_categories:
+- image-segmentation
+- semantic-segmentation
+language:
+- en
+tags:
+- material-segmentation
+- apple-dms
+- scene-parsing
+- segformer
+size_categories:
+- 10k<n<100k
+---
+
+# Apple Dense Material Segmentation (DMS) Dataset
+
+This dataset is a reconstructed version of the **Apple Dense Material Segmentation (DMS)** dataset, originally introduced in the ECCV 2022 paper *"A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing"*.
+
+It pairs high-quality dense material masks (provided by Apple) with the corresponding real-world images from the Open Images dataset.
+
+**Repository:** [AllanK24/apple-dms-materials](https://huggingface.co/datasets/AllanK24/apple-dms-materials)
+
+## Dataset Summary
+
+- **Total Samples:** ~41,385 image-mask pairs
+- **Classes:** 46 material categories (e.g., Wood, Glass, Plastic, Metal, Fabric)
+- **Task:** Semantic Segmentation
+- **Origin:** Reassembled from [Apple's Official Repository](https://github.com/apple/ml-dms-dataset) and Open Images archives.
+
+### Missing Data Note
+This dataset was reconstructed in 2026. Due to "internet rot" (images being deleted from Flickr and Open Images S3 buckets over the years), approximately **~6% of the original images are permanently lost**. 
+- **Original Dataset Size:** ~44,500 images
+- **Current Dataset Size:** ~41,385 images
+- **Status:** The dataset is ~94% complete and fully functional for training state-of-the-art segmentation models.
+
+## Usage
+
+You can load this dataset directly using the Hugging Face `datasets` library.
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("AllanK24/apple-dms-materials")
+
+# Inspect a single example
+example = dataset["train"][0]
+image = example["image"]
+mask = example["label"]
+image_id = example["image_id"]
+
+print(f"Loaded image {image_id} with size {image.size}")
+
+```
+
+### Class Labels & Taxonomy
+
+The dataset includes a `class_info.json` file containing the mapping between label IDs and material names.
+
+* **Background/Void:** Depending on the configuration, ID 0 or 255 is often used for void. Check `class_info.json` for the exact ID mapping.
+* **Classes:** 46 standard materials.
+
+## Dataset Structure
+
+The dataset is divided into three standard splits:
+
+| Split | Samples |
+| --- | --- |
+| `train` | ~30,000 |
+| `validation` | ~5,000 |
+| `test` | ~6,000 |
+
+## Licensing
+
+This dataset is a composite of two sources with different licenses:
+
+1. **Annotations (Masks):** Created by Apple. Distributed under the license specified in the [Apple ML-DMS Repository](https://github.com/apple/ml-dms-dataset) (Copyright © 2022 Apple Inc.).
+2. **Images:** Sourced from Open Images (Flickr). These images retain their original Creative Commons licenses (typically CC-BY 2.0 or CC-BY 4.0) as defined by their original photographers.
+
+**Disclaimer:** This repository is a redistribution of the reconstructed dataset for research convenience. Users are responsible for adhering to the licenses of both the image sources and the annotation providers.
+
+## Citation
+
+If you use this dataset, please cite the original Apple paper:
+
+```bibtex
+@inproceedings{upchurch2022dense,
+  title={A Dense Material Segmentation Dataset for Indoor and Outdoor Scene Parsing},
+  author={Upchurch, Paul and Niu, Ransen},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2022}
+}
+
+```
+
+```
+
+```