Update README.md

README.md

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----
-license: mit
----
+
+---
+license: mit
+task_categories:
+  - object-detection
+  - image-segmentation
+  - feature-extraction
+  - zero-shot-classification
+tags:
+  - agriculture
+  - coffee
+  - food-quality
+  - commodity-grading
+pretty_name: Pre-Roast Arabica Coffee Bean Grading Dataset
+size_categories:
+  - 1K<n<10K
+---
+
+# A Image Dataset of Pre-Roast Arabica Coffee Beans with Polygon Annotations for Automated Grading
+
+## Abstract
+Automated quality assessment of raw agricultural products is critical for ensuring fair trade and supply chain efficiency. This dataset presents **3,877 high-resolution images** of *pre-roast Arabica coffee beans*, collected from farms in **Coorg, Karnataka (India)**—a major coffee-producing region. Each bean is categorized into one of four quality grades:
+
+| Grade | Description |
+|-------|-------------|
+| A | Premium |
+| B | Good |
+| C | Standard |
+| D | Defective |
+
+A total of **2,284 beans** are annotated using **polygon masks** and grade labels in **LabelMe JSON format**, supporting research in **classification, instance segmentation, automated grading, and defect detection**. A YOLOv11 multi-class instance segmentation baseline was trained to validate annotation quality and demonstrate practical model performance.
+
+---
+
+## Dataset Structure
+
+```text
+/
+├── CGA/   # Grade A (Premium)
+│   ├── CGA_images/
+│   └── CGA_json/
+├── CGB/   # Grade B (Good)
+├── CGC/   # Grade C (Standard)
+└── CGD/   # Grade D (Defective)
+```
+
+
+### Distribution Summary
+
+| Grade | Quality | Images | Polygon Annotations |
+|-------|---------|--------|---------------------|
+| A | Premium | 1000 | 600 |
+| B | Good | 1000 | 600 |
+| C | Standard | 1002 | 487 |
+| D | Defective | 875 | 597 |
+| **Total** | — | **3,877** | **2,284** |
+
+---
+
+## Data Collection and Annotation
+
+| Attribute | Details |
+|----------|---------|
+| Coffee Variety | *Coffea arabica* |
+| Region | Coorg (Kodagu), Karnataka, India |
+| Imaging Setup | Controlled indoor lighting with a white backdrop |
+| Devices Used | iPhone 15 Pro, Samsung 2023/24 models, Google Pixel 7, Poco X Series |
+| Annotation Tool | LabelMe (Polygon Mode) |
+| Data Format | `.jpg` images + `.json` polygon annotation files |
+
+Each annotation file contains:
+* `"label": "grade_a" | "grade_b" | "grade_c" | "grade_d"`
+* `"points": [ [x1,y1], [x2,y2], ... ]`
+
+Annotations were reviewed by trained annotators to ensure precision.
+
+---
+
+## Recommended Use Cases
+
+- Multi-class instance segmentation training
+- Automated grading and sorting systems
+- Agricultural defect detection research
+- Food quality assurance studies
+- Robust low-cost supply chain inspection systems
+
+### Out-of-Scope Use
+- Personal identification
+- Medical or biometric inference (dataset contains **no personal data**)
+
+---
+
+## Baseline Experiment (YOLOv11 Segmentation)
+
+To validate the dataset's quality and visual separability, a baseline instance segmentation model (YOLOv11) was trained. The model was trained for 150 epochs, with the best-performing checkpoint saved for evaluation.
+
+The results confirm that the dataset supports strong performance for automated grading, achieving a mean Average Precision (mAP) of **0.93** for object detection and **0.91** for instance segmentation.
+
+### Final Metrics (from `best.pt` model)
+
+| Metric | Grade A | Grade B | Grade C | Grade D | Mean (All Classes) |
+|:-------|:--------:|:--------:|:--------:|:--------:|:------:|
+| **Box mAP@0.5** | 0.94 | 0.90 | 0.91 | 0.97 | **0.93** |
+| **Mask mAP@0.5** | 0.92 | 0.88 | 0.88 | 0.96 | **0.91** |
+
+> **Note:** The high precision on Grade D (Defective) is particularly valuable, as it demonstrates the model's reliability in identifying and sorting out low-quality beans, which is a primary goal of automated grading systems.
+
+---
+
+
+## How to Use
+
+### Load JSON Annotation Example
+```python
+import json
+import glob
+
+# Example for Grade A
+files = glob.glob("CGA/CGA_json/*.json")
+with open(files[0], "r") as f:
+    ann = json.load(f)
+
+print(ann["shapes"][0]["points"])  # Polygon coordinates
+print(ann["shapes"][0]["label"])   # Grade label
+````
+
+## Value of the Dataset
+
+  * First publicly available polygon-annotated dataset of pre-roast coffee beans.
+  * Enables end-to-end automated grading using segmentation + classification.
+  * Facilitates fair pricing and quality transparency in the coffee supply chain.
+  * Robust for deployment in low-cost rural environments using consumer smartphones.
+
+## Contributors
+
+| Name | ORCID | Role |
+|---|---|---|
+| Samruddh K | 0009-0008-3588-9272 | Research, Dataset Preparation & Documentation |
+| Abhay Varun S | 0009-0003-1299-724X | Research, Dataset Collection & Annotation |
+| Bopanna K N | 0009-0008-0432-3196 | Annotation Support & Verification |
+| H A Dheemanth Gowda | 0009-0001-1891-632X | Annotation Support & Verification |
+
+
+## Citation
+
+If you use this dataset, please cite:
+
+@dataset{pre_roast_coffee_grading_2025,
+title = {A Image Dataset of Pre-Roast Arabica Coffee Beans with Polygon Annotations for Automated Grading},
+author = {Samruddh K and Bopanna K N and H A Dheemanth Gowda and Abhay Varun S},
+year = {2025},
+publisher = {Hugging Face Datasets},
+license = {MIT},
+url = {https://huggingface.co/datasets/SamruddhK/coffee-bean-grading-dataset}
+}
+
+
+## Contact
+
+For questions, collaborations, or research use:
+- Dataset Maintainer: **Samruddh K & Abhay Varun S**
+- Hugging Face: https://huggingface.co/SamruddhK
+- GitHub Samruddh K: https://github.com/SAMRUDDH15
+- GitHub Abhay Varun S: https://github.com/abhay-error
+* **Email:** [samruddh.k52@gmail.com & Abhayvarun618@gmail.com]
+
+<!-- end list -->
+