Update README.md

README.md

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-license: cc-by-nc-nd-4.0
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+---
+license: cc-by-nc-nd-4.0
+task_categories:
+  - object-detection
+  - image-classification
+  - image-segmentation
+  - visual-question-answering
+size_categories:
+  - n<1K
+tags:
+  - trash-detection
+  - garbage-detection
+  - waste-classification
+  - material-classification
+  - object-detection
+  - computer-vision
+  - sustainability
+  - recycling
+  - smart-city
+  - lmm
+  - vqa
+pretty_name: Domestic Trash / Garbage Dataset (Sample)
+---
+
+# Domestic Trash / Garbage Dataset — Sample
+
+> ⚠️ **This is a free sample subset for evaluation purposes only.**  
+> The full dataset (9,000+ annotated images, multiple formats) is available for commercial licensing.  
+> **Contact:** [sales@datacluster.ai](mailto:sales@datacluster.ai) · [datacluster.ai](https://datacluster.ai)
+
+---
+
+## Dataset Summary
+
+This dataset contains real-world images of domestic trash and garbage, captured on mobile phones across diverse urban and rural environments throughout India. It is well-suited for training trash detection, material classification, and garbage segregation models used in smart waste management, automated recycling lines, municipal cleanliness monitoring, eco-friendly consumer apps, and carbon-footprint estimation systems.
+
+It is also **optimized for Generative AI, Visual Question Answering (VQA), Image Classification, and Large Multimodal Model (LMM) development**, providing a strong basis for achieving robust model performance on real-world waste imagery.
+
+Scenes cover a wide variety of capture scenarios — household trash bins, street-side garbage piles, dump yards, recycling centers, public spaces, and indoor waste containers — under varied lighting conditions (day, night), distances, and material viewpoints. The dataset captures the full diversity of trash items found in Indian domestic settings, including plastics, paper, metals, organic waste, and mixed materials.
+
+## Classes
+
+* `trash`
+
+> The full dataset additionally supports **material classification** (e.g., plastic, paper, metal, organic, glass, etc.) for more granular labeling. Contact [sales@datacluster.ai](mailto:sales@datacluster.ai) for details on material-level annotations.
+
+## Sample vs. Full Dataset
+
+|  | Sample (this repo) | Full Dataset |
+| --- | --- | --- |
+| Images | ~200 (subset) | 9,000+ |
+| Annotation formats | Pascal VOC (XML) — other formats available on request | COCO, YOLO, Pascal VOC, TF-Record |
+| Material-level labels | ❌ Not included | ✅ Available |
+| Locations covered | Representative subset | 500+ cities across India |
+| Resolution | HD (1920×1080 and above) | 99.9% HD and above (1920×1080+) |
+| Scene diversity | Representative subset | Full range (indoor, outdoor, day, night, close, far) |
+| Commercial use | ❌ Not permitted | ✅ With license |
+| Redistribution | ❌ Not permitted | Per license terms |
+| Updates | One-time | Ongoing |
+
+**To license the full dataset:** [sales@datacluster.ai](mailto:sales@datacluster.ai)
+
+## Dataset Structure
+
+```
+domestic-trash-garbage-dataset/
+├── images/                  # JPG images
+│   ├── image_0001.jpg
+│   └── ...
+└── annotations/             # Pascal VOC XML annotations (one per image)
+    ├── image_0001.xml
+    └── ...
+```
+
+Each XML file contains bounding-box annotations in the Pascal VOC format around the trash/garbage regions, with filenames matching their corresponding images.
+
+> **Need a different annotation format?** This sample ships in Pascal VOC (XML) only. YOLO, COCO, and TF-Record versions are available on request — see the conversion snippet below, or contact [sales@datacluster.ai](mailto:sales@datacluster.ai).
+
+## Data Collection
+
+* **Source:** Real-world mobile phone captures, crowdsourced from 2,000+ contributors
+* **Locations:** 500+ cities across urban and rural India
+* **Capture period:** 2020–2022
+* **Resolution:** 99.9% HD and above (1920×1080+)
+* **Conditions:** Indoor and outdoor scenes; varied lighting (day, night), weather, distances, and material viewpoints
+* **Quality:** All images are exclusively owned by DataCluster Labs (not scraped from the internet) and each image is manually reviewed and verified by computer vision professionals at DC Labs
+* **Use cases:** Trash detection, material classification, garbage segregation, smart waste management, automated recycling, municipal cleanliness monitoring, eco-friendly product recommendations, carbon-footprint estimation, VQA on waste scenes, and Large Multimodal Model (LMM) training
+
+## How to Use
+
+### Download
+
+```bash
+# Using the Hugging Face CLI
+huggingface-cli download Dataclusterlabspvtltd/Domestic-Trash-Garbage-Dataset --repo-type dataset --local-dir ./domestic-trash-garbage-dataset
+```
+
+Or clone directly:
+
+```bash
+git lfs install
+git clone https://huggingface.co/datasets/Dataclusterlabspvtltd/Domestic-Trash-Garbage-Dataset
+```
+
+### Convert VOC to YOLO or COCO
+
+The sample ships in Pascal VOC format. Convert easily with `pylabel`:
+
+```python
+from pylabel import importer
+
+# VOC → YOLO
+dataset = importer.ImportVOC(path="annotations")
+dataset.export.ExportToYoloV5(output_path="annotations_yolo")
+
+# VOC → COCO
+dataset.export.ExportToCoco(output_path="annotations_coco/annotations.json")
+```
+
+## License
+
+This sample dataset is released under the **Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)** license.
+
+Key points:
+
+* ✅ Free to download and evaluate
+* ✅ Free for academic and non-commercial research with attribution
+* ❌ No commercial use without a license from DataCluster Labs
+* ❌ No derivative works or modifications for redistribution
+* ❌ No use in training commercial ML models
+
+For commercial licensing of the full dataset, contact **[sales@datacluster.ai](mailto:sales@datacluster.ai)**.
+
+## Citation
+
+If you use this dataset in academic work, please cite:
+
+```bibtex
+@misc{datacluster_domestic_trash_sample,
+  title        = {Domestic Trash / Garbage Dataset (Sample)},
+  author       = {DataCluster Labs},
+  year         = {2026},
+  howpublished = {\url{https://huggingface.co/datasets/Dataclusterlabspvtltd/Domestic-Trash-Garbage-Dataset}},
+  note         = {Sample subset. Full dataset available for commercial licensing at sales@datacluster.ai}
+}
+```
+
+## About DataCluster Labs
+
+DataCluster Labs specializes in managed crowd-sourced data collection and annotation — images, videos, audio, text, and surveys — through our Dailydata platform. We deliver custom datasets for computer vision, NLP, and ML use cases, with a strong focus on India-first data that captures the diversity of real-world conditions across the subcontinent.
+
+📧 **Sales / Full Dataset Access:** [sales@datacluster.ai](mailto:sales@datacluster.ai)  
+🌐 **Website:** [datacluster.ai](https://datacluster.ai)