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license: mit
---
# LabOS Segmentation Dataset
A curated instance segmentation dataset of laboratory equipment that **foundation models (SAM,Gemini,YoloWorld,Grounded) consistently struggle with** and used to cover their gaps — 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.
---
## 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.
---
## Dataset Statistics
### Split Summary
| Split | Images | Annotations |
|------------|-------:|------------:|
| Train | 228 | 2,736 |
| Validation | 57 | 579 |
| **Total** |**285** | **3,315** |
Split ratio: ~80 / 20 (train / val).
### Annotations per Category
| Category | Train | Val | **Total** |
|---|---:|---:|---:|
| 14ml rack hole | 1,263 | 59 | **1,322** |
| rack 50ml hole | 506 | 258 | **764** |
| 50ml eppendorf tube | 182 | 67 | **249** |
| 50Ml eppendorf orange cap | 108 | 34 | **142** |
| 14ml round bottom tube top | 172 | 7 | **179** |
| 50Ml eppendorf orange cap top| 91 | 30 | **121** |
| 50Ml rack | 66 | 31 | **97** |
| Vortex Genie 2 | 72 | 21 | **93** |
| Vortex Genie Top Plate | 59 | 14 | **73** |
| Vortex Genie Hole | 54 | 14 | **68** |
| 50Ml eppendorf cap | 47 | 3 | **50** |
| 50Ml eppendorf blue cap | 26 | 22 | **48** |
| 50Ml eppendorf cap top | 40 | 3 | **43** |
| 14ml rack | 33 | 2 | **35** |
| 50Ml eppendorf blue cap top | 17 | 14 | **31** |
| **Total** | **2,736** | **579** | **3,315** |
---
## File Structure
```
dataset-2/
├── images/ # 285 PNG images (1280×720)
├── 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
```
---
## 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 |
|---|---|
|  Red | Vortex Genie 2 |
|  Orange | Vortex Genie Hole |
|  Yellow | Vortex Genie Top Plate |
|  Green | 50ml eppendorf tube |
|  Blue | 50Ml eppendorf cap |
|  Purple | 50Ml rack |
|  Teal | rack 50ml hole |
|  Gold | 14ml round bottom tube top |
|  Light blue | 14ml rack hole |
|  Violet | 14ml rack |
|  Dark orange | Orange cap / orange cap top |
|  Dark blue | Blue cap / blue cap top |
### Small scene — vortex hole present, 7 classes (8 instances)
### Full lab scene — vortex genie + 14ml rack + 50ml tubes (113 instances)
### Vortex genie + 14ml rack with holes and tube tops (44 instances)
### 50ml rack — blue and orange caps, rack holes, no vortex (16 instances)
### Vortex top plate + orange caps + rack holes (36 instances)
### Dense 50ml rack — blue, orange & generic caps with rack holes (81 instances)
### Vortex genie + orange caps, no rack holes (27 instances)
### Blue caps focus — rack holes and tube bodies (42 instances)
### 14ml rack + vortex genie — large annotation count (130 instances)
---
## Pre-trained Weights
`segment-yolo-weights.pt` — YOLO segmentation model trained on this dataset. Load with:
```python
from ultralytics import YOLO
model = YOLO("segment-yolo-weights.pt")
results = model("images/1.png")
```
---
## License
MIT — see license field above.
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