---
license: cc-by-nc-4.0
---

# LabOS Segmentation Dataset

A curated instance segmentation dataset of laboratory equipment that foundation models typically struggle with, and used the following to cover their gaps for LabOS tasks — including vortex genies, eppendorf tubes, multi-tube racks, colored caps, and fine-grained sub-parts like rack holes, tube tops, and mixer plates.

Annotations are provided in both **COCO JSON** and **YOLO polygon** formats.

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## Why This Dataset?

General-purpose vision models fail on lab equipment for several reasons:

- **Repetitive, nearly-identical sub-objects** — racks with dozens of uniform holes challenge, most foundation models have failed at, both detection and counting.
- **Transparent / translucent materials** — eppendorf tubes and caps have subtle visual boundaries.
- **Fine-grained part segmentation** — distinguishing a vortex genie *top plate* from its body, or an *orange cap top* from its barrel, requires part-level understanding that VLMs lack.
- **Domain specificity** — lab bench imagery is severely underrepresented in web-scraped pre-training data.

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## Dataset Statistics

### Split Summary

| Split      | Images | Annotations |
|------------|-------:|------------:|
| Train      |    251 |       2,989 |
| Validation |     63 |         682 |
| **Total**  |**314** |   **3,671** |

Split ratio: ~80 / 20 (train / val).

### Annotations per Category

| Category | Train | Val | **Total** |
|---|---:|---:|---:|
| 14ml rack hole                 | 1,263 |  59 | **1,322** |
| 4 way rack 50ml hole           |   575 | 292 | **867** |
| 50ml eppendorf tube            |   235 |  85 | **320** |
| 14ml round bottom tube top     |   172 |   7 | **179** |
| 50Ml eppendorf orange cap      |   135 |  44 | **179** |
| 50Ml eppendorf orange cap top  |   106 |  37 | **143** |
| 50Ml 4 way rack                |    85 |  35 | **120** |
| Vortex Genie 2                 |    83 |  25 | **108** |
| Vortex Genie Top Plate         |    67 |  17 | **84** |
| 50Ml eppendorf blue cap        |    49 |  31 | **80** |
| Vortex Genie Hole              |    60 |  17 | **77** |
| 50Ml eppendorf blue cap top    |    39 |  25 | **64** |
| 50Ml eppendorf cap             |    47 |   3 | **50** |
| 50Ml eppendorf cap top         |    40 |   3 | **43** |
| 14ml rack                      |    33 |   2 | **35** |
| **Total**                   | **2,989** | **682** | **3,671** |

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## File Structure

```
dataset-2/
├── images/                    # 314 PNG images (mixed 1280x720 and 1920x1200)
├── labels/                    # polygon segmentation (.txt, one per image)
├── annotations.json           # COCO format — all images
├── annotations_train.json     # COCO format — training split
├── annotations_val.json       # COCO format — validation split
├── dataset.yaml               # dataset config
└── demo_imgs/                 # Annotated visualization examples
```

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## Annotation Format

**COCO JSON** — bounding boxes + polygon segmentation masks per instance.

**YOLO TXT** — one file per image, each line:
```
<class_id> x1 y1 x2 y2 ... xN yN
```
Coordinates are normalized to `[0, 1]`. Annotations were created and exported from **CVAT**.

---

## Example Visualizations

| Color | Category |
|---|---|
| ![#ff4646](https://placehold.co/12x12/ff4646/ff4646) Red | Vortex Genie 2 |
| ![#ffa000](https://placehold.co/12x12/ffa000/ffa000) Orange | Vortex Genie Hole |
| ![#d2c300](https://placehold.co/12x12/d2c300/d2c300) Yellow | Vortex Genie Top Plate |
| ![#32c832](https://placehold.co/12x12/32c832/32c832) Green | 50ml eppendorf tube |
| ![#3282ff](https://placehold.co/12x12/3282ff/3282ff) Blue | 50Ml eppendorf cap |
| ![#aa32ff](https://placehold.co/12x12/aa32ff/aa32ff) Purple | 50Ml rack |
| ![#32dcb4](https://placehold.co/12x12/32dcb4/32dcb4) Teal | rack 50ml hole |
| ![#ffc864](https://placehold.co/12x12/ffc864/ffc864) Gold | 14ml round bottom tube top |
| ![#64b4ff](https://placehold.co/12x12/64b4ff/64b4ff) Light blue | 14ml rack hole |
| ![#c864ff](https://placehold.co/12x12/c864ff/c864ff) Violet | 14ml rack |
| ![#ff8c1e](https://placehold.co/12x12/ff8c1e/ff8c1e) Dark orange | Orange cap / orange cap top |
| ![#1e8cff](https://placehold.co/12x12/1e8cff/1e8cff) Dark blue | Blue cap / blue cap top |


### Small scene — vortex hole present, 7 classes (8 instances)
![demo 27](demo_imgs/demo_27.png)

### Full lab scene — vortex genie + 14ml rack + 50ml tubes (113 instances)
![demo 280](demo_imgs/demo_280.png)

### Vortex genie + 14ml rack with holes and tube tops (44 instances)
![demo 274](demo_imgs/demo_274.png)

### 50ml rack — blue and orange caps, rack holes, no vortex (16 instances)
![demo 7](demo_imgs/demo_7.png)

### Vortex top plate + orange caps + rack holes (36 instances)
![demo 177](demo_imgs/demo_177.png)

### Dense 50ml rack — blue, orange & generic caps with rack holes (81 instances)
![demo 29](demo_imgs/demo_29.png)

### Vortex genie + orange caps, no rack holes (27 instances)
![demo 234](demo_imgs/demo_234.png)

### Blue caps focus — rack holes and tube bodies (42 instances)
![demo 82](demo_imgs/demo_82.png)

### 14ml rack + vortex genie — large annotation count (130 instances)
![demo 285](demo_imgs/demo_285.png)



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## Pre-trained Weights

`robot-segmentation.pt` — YOLO segmentation model trained on this dataset, mirrored from [`LabOS-Robot-Runtime`](https://github.com/cong-lab/LabOS-Robot-Runtime/blob/main/weights/robot-segmentation.pt). Load with:

```python
from ultralytics import YOLO
model = YOLO("robot-segmentation.pt")
results = model("images/1.png")
```

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## License

This dataset and accompanying weights are released for non-commercial research use under the Creative Commons Attribution-NonCommercial 4.0 license (`CC-BY-NC-4.0`).