README.md

---
task_categories:
- object-detection
license: cc-by-nc-4.0
tags:
- Materials Informatics
- AI for Science
dataset_info:
  features:
  - name: image
    dtype: image
  - name: image_id
    dtype: int64
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: bbox
    list:
      list: float64
  - name: category
    list: string
  - name: category_id
    list: int64
  - name: area
    list: float64
  - name: iscrowd
    list: int64
  splits:
  - name: train
    num_bytes: 2518645352
    num_examples: 2855
  download_size: 2321915601
  dataset_size: 2518645352
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
pretty_name: MaterialScope
arxiv: 2606.29667
---

# MaterialScope

A manually annotated object detection dataset for **compound material image to sub-image region detection**, in **COCO format**.  
All 2,855 images are fully annotated — no unannotated images in the split.

---

## Dataset Summary

| Metric | Value |
|---|---|
| Total images | 2,855 |
| Total annotations | 13,008 |
| Categories | 23 |
| Avg. annotations per image | 4 |
| Max annotations per image | 21 |
| Annotation format | COCO JSON |
| Image format | JPG |

---

## Category Distribution

| Category | Annotation Count |
|---|---|
| A | 2,552 |
| B | 2,539 |
| C | 2,192 |
| D | 1,979 |
| E | 1,145 |
| F | 867 |
| G | 436 |
| H | 346 |
| I | 219 |
| J | 109 |
| K | 85 |
| L | 64 |
| M | 33 |
| N | 26 |
| O | 19 |
| P | 8 |
| Q | 5 |
| R | 4 |
| S | 3 |
| T | 3 |
| single | 272 |
| common | 41 |
| unlabel | 61 |
| **Total** | **13,008** |

---

## File Structure

```
merged_coco.json   ← COCO annotation file (images + annotations + categories)
images/            ← 2,855 JPG images

```

---

## Annotation Format

Annotations follow the standard [COCO format](https://cocodataset.org/#format-data):

```json
{
  "images": [
    { "id": 1, "file_name": "img1.jpg", "width": 1721, "height": 781 }
  ],
  "annotations": [
    {
      "id": 1,
      "image_id": 1,
      "category_id": 21,
      "bbox": [x, y, width, height],
      "area": 1318264.48,
      "iscrowd": 0
    }
  ],
  "categories": [
    { "id": 1, "name": "A" },
    { "id": 2, "name": "B" },
    ...
  ]
}
```

Bounding boxes are in `[x_min, y_min, width, height]` format (COCO standard).

---

## Loading the Dataset

### With Python (raw COCO)

```python
import json
from PIL import Image

with open("merged_coco.json") as f:
    coco = json.load(f)

# Build a lookup: image_id → annotations
from collections import defaultdict
ann_by_image = defaultdict(list)
for ann in coco["annotations"]:
    ann_by_image[ann["image_id"]].append(ann)

# Load an image and its annotations
img_info = coco["images"][0]
image    = Image.open(f"images/{img_info['file_name']}")
anns     = ann_by_image[img_info["id"]]
print(f"{img_info['file_name']}: {len(anns)} annotations")
```

### With pycocotools

```python
from pycocotools.coco import COCO

coco = COCO("merged_coco.json")
img_ids = coco.getImgIds()
ann_ids = coco.getAnnIds(imgIds=img_ids[0])
anns    = coco.loadAnns(ann_ids)
```

### With Ultralytics (YOLO training)

```bash
pip install ultralytics
```

```python
from ultralytics.data.converter import convert_coco

convert_coco(
    labels_dir=".",
    save_dir="yolo_dataset",
    use_segments=False,
)
```

---

## License

This dataset is licensed under **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)**.

- Free to use for research and non-commercial purposes
- You must give appropriate credit when using or sharing this dataset
- Derivatives must be shared under the same license
- Commercial use is **not permitted**

Full license text: https://creativecommons.org/licenses/by-nc/4.0/

---

## Citation

If you use this dataset in your research, please cite it as:

```bibtex
@article{ghosh2026unlocking,
  title={Unlocking the Visual Record of Materials Science: A Large-Scale Multimodal Dataset from Scientific Literature},
  author={Ghosh, Subham and Tiwari, Shubham and Ibrahim, Mohammad and Tewari, Abhishek},
  journal={arXiv preprint arXiv:2606.29667},
  year={2026},
  doi={https://doi.org/10.48550/arXiv.2606.29667}
}

```