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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 \|
	\|---\|---\|
	\| ![#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)



	---

	## 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.