README.md

---
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
---

# An Image Dataset of Pre-Roast Robusta 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 robusta* |
| 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]

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