Datasets:
cls stringclasses 6
values | bbox listlengths 4 4 | visible float64 0.08 1 | liquid stringclasses 8
values | fill float64 0 0.85 | scale float64 0.8 1.4 |
|---|---|---|---|---|---|
erlenmeyer_flask | [
0.2908277065,
0.672551088,
0.1591514647,
0.1800744385
] | 0.8 | water | 0.31 | 0.92 |
graduated_cylinder | [
0.6887346804,
0.2929635644,
0.1525885463,
0.338160634
] | 0.64 | blue | 0.37 | 0.92 |
petri_dish | [
0.1757944636,
0.3661171496,
0.1978528872,
0.1619431376
] | 1 | red | 0.5 | 1.33 |
beaker | [
0.4924047887,
0.4580959082,
0.0624020696,
0.0807440281
] | 1 | yellow | 0.5 | 1.19 |
petri_dish | [
0.542316556,
0.5508423001,
0.0689804554,
0.0507782996
] | 1 | none | 0 | 0.99 |
erlenmeyer_flask | [
0.6344107389,
0.5958522111,
0.1055566072,
0.1416776478
] | 0.72 | yellow | 0.62 | 1.34 |
test_tube | [
0.7477271557,
0.5016601384,
0.1462310553,
0.0591025949
] | 0.56 | red | 0.68 | 1.03 |
beaker | [
0.1179281604,
0.5682897121,
0.1645179428,
0.1805658638
] | 1 | blue | 0.56 | 1.27 |
beaker | [
0.8081127405,
0.5227683932,
0.0878335238,
0.0996288359
] | 1 | amber | 0.42 | 0.82 |
test_tube | [
0.4183172733,
0.3723261654,
0.0322350562,
0.1900683045
] | 0.96 | none | 0 | 1.37 |
petri_dish | [
0.4506749511,
0.7618030384,
0.150883317,
0.1179381162
] | 1 | red | 0.5 | 0.93 |
graduated_cylinder | [
0.3545365185,
0.482913658,
0.0869859159,
0.2262043655
] | 0.56 | amber | 0.34 | 1.15 |
beaker | [
0.5718207359,
0.4536814988,
0.0535863638,
0.0698437095
] | 1 | water | 0.19 | 0.93 |
beaker | [
0.508551687,
0.4767404795,
0.0599482656,
0.0695437193
] | 0.68 | purple | 0.78 | 0.89 |
round_bottom_flask | [
0.5053174347,
0.5512373,
0.0961741507,
0.1518127024
] | 0.72 | purple | 0.58 | 0.95 |
beaker | [
0.6382817328,
0.617268011,
0.0843099952,
0.104269594
] | 1 | blue | 0.22 | 0.8 |
petri_dish | [
0.3252032548,
0.6139622033,
0.160713464,
0.0823399425
] | 1 | none | 0 | 1.36 |
round_bottom_flask | [
0.6324418783,
0.512067914,
0.1351059675,
0.2135486603
] | 0.56 | amber | 0.18 | 1.35 |
round_bottom_flask | [
0.7690673769,
0.5966011882,
0.0974081159,
0.1483677626
] | 0.68 | amber | 0.33 | 0.83 |
round_bottom_flask | [
0.8929646313,
0.921292536,
0.2140707374,
0.1574149281
] | 0.76 | red | 0.55 | 1.36 |
round_bottom_flask | [
0.9043072164,
0.7410089597,
0.1913855672,
0.1907256693
] | 0.88 | red | 0.59 | 0.85 |
round_bottom_flask | [
0.9028568864,
0.4369631857,
0.133548975,
0.1995268762
] | 0.68 | blue | 0.53 | 1.07 |
erlenmeyer_flask | [
0.5746267736,
0.4664894193,
0.055501163,
0.0681394041
] | 0.72 | none | 0 | 0.82 |
petri_dish | [
0.608922869,
0.5425289124,
0.0753869414,
0.0699593723
] | 1 | yellow | 0.5 | 1.02 |
round_bottom_flask | [
0.5848940015,
0.4576749802,
0.0430208445,
0.0646467209
] | 0.76 | none | 0 | 1.02 |
erlenmeyer_flask | [
0.4360386878,
0.4849165976,
0.0552269518,
0.0735622048
] | 0.8 | red | 0.16 | 1.14 |
test_tube | [
0.4958443344,
0.5909436941,
0.0819436908,
0.0266531706
] | 0.92 | green | 0.23 | 1.02 |
beaker | [
0.5407115817,
0.5172611922,
0.0411776304,
0.0546650589
] | 1 | amber | 0.2 | 0.95 |
round_bottom_flask | [
0.4927451909,
0.5100895166,
0.0532709956,
0.0785988569
] | 0.72 | purple | 0.81 | 1.14 |
erlenmeyer_flask | [
0.4845373929,
0.4768576175,
0.0407938361,
0.054014951
] | 0.4 | red | 0.82 | 0.86 |
erlenmeyer_flask | [
0.3344377279,
0.5465230644,
0.133261025,
0.1792250276
] | 0.68 | purple | 0.47 | 0.93 |
test_tube | [
0.3932980448,
0.4003371596,
0.1216792166,
0.0793462992
] | 0.96 | blue | 0.6 | 1.03 |
test_tube | [
0.0474358934,
0.5074044466,
0.0844946527,
0.1733579636
] | 0.96 | yellow | 0.37 | 0.94 |
petri_dish | [
0.6794195473,
0.4449189007,
0.1549455523,
0.1036646962
] | 1 | yellow | 0.5 | 1.26 |
beaker | [
0.0998639623,
0.6838328168,
0.195816068,
0.2136229724
] | 1 | purple | 0.35 | 1.09 |
round_bottom_flask | [
0.3939828277,
0.4917758107,
0.0311818719,
0.0471469164
] | 0.8 | yellow | 0.3 | 0.83 |
beaker | [
0.5726641715,
0.5142395496,
0.0356782079,
0.0459769964
] | 0.92 | yellow | 0.54 | 0.97 |
beaker | [
0.4511582255,
0.523261115,
0.0524247885,
0.0681231916
] | 0.96 | green | 0.21 | 1.37 |
erlenmeyer_flask | [
0.2131235451,
0.2257562876,
0.4262470901,
0.4515125751
] | 0.64 | purple | 0.37 | 1.24 |
erlenmeyer_flask | [
0.5739875883,
0.1322870255,
0.2837878764,
0.2645740509
] | 0.76 | blue | 0.31 | 0.83 |
beaker | [
0.3677449375,
0.4585224986,
0.1357425749,
0.16061306
] | 1 | none | 0 | 1.15 |
erlenmeyer_flask | [
0.8367022574,
0.5012735128,
0.1623788476,
0.2095885277
] | 0.8 | amber | 0.7 | 1 |
erlenmeyer_flask | [
0.6368596852,
0.4071381986,
0.158932507,
0.2157039046
] | 0.72 | green | 0.2 | 1.3 |
erlenmeyer_flask | [
0.4063537866,
0.4373727888,
0.1550422609,
0.1995060146
] | 0.8 | green | 0.53 | 1.34 |
erlenmeyer_flask | [
0.362294063,
0.4517540187,
0.0989355743,
0.1202935874
] | 0.52 | green | 0.33 | 1.28 |
round_bottom_flask | [
0.5529832542,
0.4421787113,
0.0762802958,
0.1177900732
] | 0.76 | purple | 0.5 | 1.15 |
test_tube | [
0.7129459679,
0.5399143547,
0.0771841407,
0.0430672467
] | 1 | water | 0.51 | 0.89 |
round_bottom_flask | [
0.1991350353,
0.5056650192,
0.118219018,
0.1736190617
] | 0.64 | none | 0 | 1.04 |
graduated_cylinder | [
0.4822875559,
0.4258523136,
0.0532230735,
0.1763180792
] | 0.6 | amber | 0.29 | 0.86 |
petri_dish | [
0.2606754377,
0.5228851438,
0.1129677147,
0.0388137698
] | 0.52 | red | 0.5 | 1.19 |
graduated_cylinder | [
0.3966602832,
0.4196591675,
0.094422251,
0.3086512685
] | 0.6 | yellow | 0.62 | 1.08 |
beaker | [
0.6055419147,
0.4362961054,
0.1191601157,
0.1396292448
] | 1 | red | 0.65 | 1.13 |
beaker | [
0.5584826767,
0.4319158196,
0.0717757344,
0.0944200754
] | 1 | water | 0.48 | 1.15 |
round_bottom_flask | [
0.689846307,
0.4348181486,
0.0677345395,
0.097115159
] | 0.76 | purple | 0.72 | 1.01 |
erlenmeyer_flask | [
0.3824104369,
0.5048564076,
0.0990034938,
0.1298497915
] | 0.64 | red | 0.72 | 1.26 |
petri_dish | [
0.5333127975,
0.6557203084,
0.0937584639,
0.0881184638
] | 1 | amber | 0.5 | 1.04 |
test_tube | [
0.6225326955,
0.5289544612,
0.0885325074,
0.0451354682
] | 1 | blue | 0.62 | 0.82 |
beaker | [
0.6796128154,
0.3560206294,
0.182648778,
0.1999827623
] | 1 | yellow | 0.52 | 1.1 |
round_bottom_flask | [
0.4807662368,
0.4130633175,
0.1723707914,
0.276889503
] | 0.72 | yellow | 0.21 | 1.24 |
beaker | [
0.1488901824,
0.5390815139,
0.1424471736,
0.1587753296
] | 1 | none | 0 | 0.86 |
round_bottom_flask | [
0.3065956086,
0.3688701093,
0.1622404158,
0.2592992187
] | 0.72 | green | 0.84 | 1.25 |
test_tube | [
0.9089841247,
0.5171775669,
0.1632573605,
0.132196635
] | 0.68 | blue | 0.16 | 0.99 |
petri_dish | [
0.0863091215,
0.4284512699,
0.1706130477,
0.0940415263
] | 1 | red | 0.5 | 1.2 |
graduated_cylinder | [
0.9139451683,
0.4126040637,
0.1721096635,
0.7686768174
] | 0.64 | green | 0.55 | 1.3 |
petri_dish | [
0.1095951647,
0.7268016115,
0.2191903293,
0.1774876863
] | 1 | red | 0.5 | 1.06 |
petri_dish | [
0.5627878159,
0.6977026761,
0.1681419909,
0.1514359117
] | 1 | amber | 0.5 | 1.26 |
petri_dish | [
0.2907600105,
0.5401867628,
0.1442188621,
0.1125274897
] | 1 | yellow | 0.5 | 1.24 |
beaker | [
0.651917547,
0.4710657001,
0.0768019557,
0.0966182947
] | 1 | purple | 0.29 | 0.8 |
petri_dish | [
0.6255348623,
0.3672282994,
0.1154572368,
0.0786266923
] | 1 | red | 0.5 | 1.24 |
test_tube | [
0.6714377701,
0.5252907425,
0.1168544888,
0.0381477177
] | 1 | amber | 0.77 | 1.34 |
petri_dish | [
0.4432483912,
0.5405574292,
0.0590611696,
0.0288104117
] | 1 | none | 0 | 0.9 |
erlenmeyer_flask | [
0.7282137871,
0.4747321606,
0.0839656591,
0.110574007
] | 0.8 | water | 0.2 | 0.96 |
petri_dish | [
0.5387732089,
0.5495361239,
0.0918655992,
0.0765421093
] | 1 | amber | 0.5 | 1.3 |
round_bottom_flask | [
0.5281852782,
0.4321267307,
0.0481669307,
0.0719256997
] | 0.88 | green | 0.49 | 0.88 |
petri_dish | [
0.5448962152,
0.4483630657,
0.0495508313,
0.0361295938
] | 1 | red | 0.5 | 1.21 |
erlenmeyer_flask | [
0.4670985341,
0.4918288291,
0.0553845167,
0.0731582046
] | 0.84 | amber | 0.32 | 1.24 |
test_tube | [
0.5572547615,
0.5304671973,
0.0551310182,
0.0463706553
] | 0.88 | red | 0.69 | 1.08 |
beaker | [
0.6105976105,
0.4751925468,
0.0558395386,
0.0696617365
] | 1 | green | 0.54 | 1.4 |
petri_dish | [
0.3733493686,
0.5408795923,
0.0517213345,
0.0413278043
] | 1 | amber | 0.5 | 1.06 |
round_bottom_flask | [
0.5114972591,
0.4268324375,
0.0431793928,
0.0680713654
] | 0.56 | green | 0.59 | 1.1 |
petri_dish | [
0.5061442703,
0.50742504,
0.0519925654,
0.0264623761
] | 0.92 | none | 0 | 0.98 |
graduated_cylinder | [
0.437531516,
0.4322764575,
0.0291462243,
0.1059666276
] | 0.28 | water | 0.74 | 0.86 |
graduated_cylinder | [
0.5843831599,
0.4571165591,
0.0397263169,
0.1263089478
] | 0.72 | red | 0.2 | 0.92 |
beaker | [
0.4680330604,
0.51804097,
0.0526542366,
0.0652071536
] | 1 | water | 0.43 | 1.08 |
beaker | [
0.4658618718,
0.4459761381,
0.0485058725,
0.0577113628
] | 0.96 | blue | 0.52 | 1.27 |
erlenmeyer_flask | [
0.5293422788,
0.4948438108,
0.0893655121,
0.1114121079
] | 0.76 | yellow | 0.29 | 1.26 |
beaker | [
0.4363300055,
0.520234257,
0.0588383973,
0.0663061738
] | 1 | purple | 0.69 | 0.97 |
beaker | [
0.3625312448,
0.5163509697,
0.0652579665,
0.0786637962
] | 1 | none | 0 | 0.89 |
beaker | [
0.8085545003,
0.55155164,
0.0903211236,
0.1018214822
] | 1 | amber | 0.78 | 1.02 |
erlenmeyer_flask | [
0.7461179793,
0.5144719779,
0.0705359578,
0.0908113122
] | 0.8 | red | 0.42 | 1.06 |
petri_dish | [
0.343500182,
0.5065858066,
0.0710643232,
0.0530477166
] | 1 | red | 0.5 | 1.1 |
test_tube | [
0.6901684701,
0.5101854056,
0.0259761214,
0.0739993751
] | 1 | purple | 0.56 | 0.92 |
graduated_cylinder | [
0.4807629883,
0.4306962192,
0.0461525321,
0.1500281692
] | 0.64 | green | 0.62 | 0.98 |
graduated_cylinder | [
0.6419748962,
0.5892471075,
0.0701985955,
0.172055006
] | 0.68 | yellow | 0.22 | 0.99 |
petri_dish | [
0.6036491692,
0.5877251923,
0.0652146935,
0.0536030531
] | 0.92 | none | 0 | 0.92 |
graduated_cylinder | [
0.7387769818,
0.4110456109,
0.1618907452,
0.3612226248
] | 0.56 | red | 0.55 | 0.88 |
beaker | [
0.7289828956,
0.41778934,
0.2630746961,
0.3064906597
] | 1 | yellow | 0.32 | 1.15 |
round_bottom_flask | [
0.5283721536,
0.4984774441,
0.0955982506,
0.1452222764
] | 0.84 | none | 0 | 0.99 |
beaker | [
0.6063194275,
0.6211970598,
0.0952777863,
0.123785466
] | 1 | red | 0.55 | 0.87 |
beaker | [
0.5993509889,
0.8176028356,
0.1481636763,
0.198249802
] | 1 | yellow | 0.45 | 1.04 |
End of preview. Expand in Data Studio
QM Synthetic Lab Glassware with Liquids (6 types) - Free Sample
Buy the full commercial edition: $25 USD -> Polar checkout, instant download
This free sample is non-commercial (CC BY-NC-SA 4.0). The paid full edition has a commercial licence.
Custom dataset of YOUR object ($249)
Need data of YOUR object? Custom synthetic dataset, $249 USD -> order on Polar
- What you get: 2,000 labelled photoreal synthetic images (640x640 JPEG) of your own object or scenario (product, part, tool, drone, package, defect...), up to 3 classes, YOLO bounding boxes + data.yaml, train/val/test split, quality report
- Licence: commercial use allowed
- Price: $249 USD one-time; one round of adjustments included
- Delivery: typically 3-5 business days after we receive your reference photos + rough dimensions
- Optional sim-to-real test: send ~200 of your own labelled real images and we report how much the synthetic data improves a detector on them
- Refund: full refund if we cannot deliver your request (14-day refund policy)
- Limits: only objects you own or are allowed to use; no weapons or anything meant to harm people; no copied third-party 3D assets
- Order URL: https://buy.polar.sh/polar_cl_AQu6LzRtqiQmKPlgPt0zePBtvWjJI4vULeFMQ4NLHH9
- Details + contact: https://quailrobot-quailmodel.static.hf.space
Summary
Synthetic images of 6 types of laboratory glassware with coloured liquids, YOLO bounding boxes and per-object attributes (liquid colour, fill fraction), for lab-automation robots and liquid-level research; the paid full edition can be used commercially.
- Best for: lab-automation detection pre-training, liquid-level research, inventory demos
- Not for: precise volume reading from graduation marks
Quick start
# pip install huggingface_hub ultralytics
from huggingface_hub import snapshot_download
path = snapshot_download("quailrobot/labglass-v1-sample", repo_type="dataset")
from ultralytics import YOLO
YOLO("yolov8n.pt").train(data=f"{path}/data.yaml", epochs=50, imgsz=640)
Facts
- Task: object detection (YOLO format) + attribute labels
- Labels: 6 classes: 0 beaker, 1 erlenmeyer_flask, 2 test_tube, 3 graduated_cylinder, 4 round_bottom_flask, 5 petri_dish; liquid colour, fill fraction and scale in meta/
- Full edition size: 5,000 images 640x640 (train 3,959 / val 540 / test 501) with YOLO labels + attribute JSON (zip 106.3 MB)
- Free sample size: 300 images (train 241 / val 27 / test 32)
- Format: 640x640 JPEG images + YOLO txt labels + data.yaml (Ultralytics-ready)
- What's included (full edition): images/{train,val,test}, labels/{train,val,test}, meta/{train,val,test} (per-object attribute JSON), data.yaml, README.md, LICENSE.txt
- Price: $25 USD; checkout may display the equivalent in your local currency
- Buy URL (primary): https://buy.polar.sh/polar_cl_mk4xkc4gN3fUDteh4HJKHlSaPNe3KKFMdqcwO4fkB01
- Licence (full edition): LicenseRef-QuailModel-Commercial (commercial use allowed, no resale of the data)
- Licence (free sample): CC-BY-NC-SA-4.0
- Validation: Labels are computed from exact projected 3D mesh vertices with ray-cast occlusion checks (no hand labelling). No benchmark on real photos has been published for this dataset yet.
- Data source: 100% synthetic, generated by QuailModel with AI assistance (generator code written with an AI model)
- Catalog (all QuailModel datasets, catalog.json, llms.txt): https://quailrobot-quailmodel.static.hf.space
- Last updated: 2026-10-10
Validation
Labels are computed from exact projected 3D mesh vertices with ray-cast occlusion checks (no hand labelling). No benchmark on real photos has been published for this dataset yet.
Price & licence
- Full edition: $25 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_mk4xkc4gN3fUDteh4HJKHlSaPNe3KKFMdqcwO4fkB01
- QuailModel Commercial Dataset Licence (SPDX: LicenseRef-QuailModel-Commercial): you may train, evaluate and ship models, including in commercial products. You may not resell or redistribute the dataset itself.
- Free sample (this page): CC BY-NC-SA 4.0 - free for non-commercial use.
Limitations
- Synthetic renders with glass refraction - fine-tune on real lab photos before production.
- No graduation markings or text on glassware.
This free sample: 300 images (train 241 / val 27 / test 32). Full commercial edition: 5,000 images 640x640 (train 3,959 / val 540 / test 501) with YOLO labels + attribute JSON (zip 106.3 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.
Contents
| split | images | boxes |
|---|---|---|
| train | 241 | 912 |
| val | 27 | 99 |
| test | 32 | 115 |
- Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = beaker, 1 = erlenmeyer_flask, 2 = test_tube, 3 = graduated_cylinder, 4 = round_bottom_flask, 5 = petri_dish. Images with an empty label file are true negatives (4 of 300).
- Boxes per class: beaker 206, erlenmeyer_flask 185, test_tube 157, graduated_cylinder 191, round_bottom_flask 193, petri_dish 194
meta/holds per-object attribute JSON (class, box, visibility and module-specific attributes).data.yamlincluded - train directly with Ultralytics YOLO.- Box size distribution (fraction of image width): median 0.104, 2% of boxes are smaller than 16 px.
How it was made
Original 3D models, procedurally generated and rendered with a physically based renderer under real-world lighting, with realistic camera effects. Labels are computed exactly from the 3D scene (no hand labelling).
Credits
Lighting environments: Poly Haven HDRIs (CC0), credited.
Licence
Sample edition: CC BY-NC-SA 4.0 (non-commercial). The full commercial edition is sold by QuailModel (see the buy link).
Disclosure
Generated by QuailModel with AI assistance (generator code written with an AI model); all data is synthetic / computer-generated. Validate on your own real data before production use.
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