Datasets:

quailrobot
/

jars-v1-sample

---
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
tags:
- glass
- jar
- retail
- kitchen
- synthetic
- yolo
pretty_name: QM Synthetic Glass Jars (5 types + attributes)
size_categories:
- n<1K
---
# QM Synthetic Glass Jars (5 types + attributes) - Free Sample

<!-- qm-buy:start -->
**Buy the full commercial edition: $5 USD -> [Polar checkout, instant download](https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej)**  
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 Object-Detection Dataset of Your Object (2,000 images), $249 USD -> [order on Polar](https://buy.polar.sh/polar_cl_AQu6LzRtqiQmKPlgPt0zePBtvWjJI4vULeFMQ4NLHH9)**
- 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 photos of glass jars (5 jar types) with YOLO bounding boxes and per-object attributes (tint, lid, contents, fill level), for retail shelf, kitchen robotics and inventory models; the paid full edition can be used commercially.
- Best for: jar detection / attribute pre-training, robot grasping research, inventory demos
- Not for: recognising specific commercial brands or labels

## Quick start
```python
# pip install huggingface_hub ultralytics
from huggingface_hub import snapshot_download
path = snapshot_download("quailrobot/jars-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: 5 classes: 0 mason_jar, 1 apothecary_jar, 2 spice_jar, 3 hex_jar, 4 squat_jam_jar; attributes in meta/ (tint, lid colour/type, contents, fill level, height)
- Full edition size: 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB)
- Free sample size: 300 images (train 237 / val 34 / test 29)
- 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: $5 USD; checkout may display the equivalent in your local currency
- Buy URL (primary): https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej
- 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: $5 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej
- 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 (Cycles path tracing): fine-tune on real photos before production.
- Five original jar families only - not brand-specific products.
<!-- qm-buy:end -->

**This free sample:** 300 images (train 237 / val 34 / test 29). **Full commercial edition:** 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB).
Fully synthetic data; summary, facts, validation and limitations are in the block above.

## Contents
| split | images | boxes |
|---|---|---|
| train | 237 | 753 |
| val | 34 | 105 |
| test | 29 | 97 |

- Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = mason_jar, 1 = apothecary_jar, 2 = spice_jar, 3 = hex_jar, 4 = squat_jam_jar. Images with an empty label file are true negatives (24 of 300).
- Boxes per class: mason_jar 182, apothecary_jar 194, spice_jar 186, hex_jar 203, squat_jam_jar 190
- `meta/` holds per-object attribute JSON (class, box, visibility and module-specific attributes).
- `data.yaml` included - train directly with Ultralytics YOLO.
- Box size distribution (fraction of image width): median 0.074, 4% 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.

	---
	license: cc-by-nc-sa-4.0
	task_categories:
	- object-detection
	tags:
	- glass
	- jar
	- retail
	- kitchen
	- synthetic
	- yolo
	pretty_name: QM Synthetic Glass Jars (5 types + attributes)
	size_categories:
	- n<1K
	---
	# QM Synthetic Glass Jars (5 types + attributes) - Free Sample

	<!-- qm-buy:start -->
	Buy the full commercial edition: $5 USD -> [Polar checkout, instant download](https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej)
	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 Object-Detection Dataset of Your Object (2,000 images), $249 USD -> [order on Polar](https://buy.polar.sh/polar_cl_AQu6LzRtqiQmKPlgPt0zePBtvWjJI4vULeFMQ4NLHH9)
	- 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 photos of glass jars (5 jar types) with YOLO bounding boxes and per-object attributes (tint, lid, contents, fill level), for retail shelf, kitchen robotics and inventory models; the paid full edition can be used commercially.
	- Best for: jar detection / attribute pre-training, robot grasping research, inventory demos
	- Not for: recognising specific commercial brands or labels

	## Quick start
	```python
	# pip install huggingface_hub ultralytics
	from huggingface_hub import snapshot_download
	path = snapshot_download("quailrobot/jars-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: 5 classes: 0 mason_jar, 1 apothecary_jar, 2 spice_jar, 3 hex_jar, 4 squat_jam_jar; attributes in meta/ (tint, lid colour/type, contents, fill level, height)
	- Full edition size: 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB)
	- Free sample size: 300 images (train 237 / val 34 / test 29)
	- 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: $5 USD; checkout may display the equivalent in your local currency
	- Buy URL (primary): https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej
	- 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: $5 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej
	- 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 (Cycles path tracing): fine-tune on real photos before production.
	- Five original jar families only - not brand-specific products.
	<!-- qm-buy:end -->

	This free sample: 300 images (train 237 / val 34 / test 29). Full commercial edition: 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB).
	Fully synthetic data; summary, facts, validation and limitations are in the block above.

	## Contents
	\| split \| images \| boxes \|
	\|---\|---\|---\|
	\| train \| 237 \| 753 \|
	\| val \| 34 \| 105 \|
	\| test \| 29 \| 97 \|

	- Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = mason_jar, 1 = apothecary_jar, 2 = spice_jar, 3 = hex_jar, 4 = squat_jam_jar. Images with an empty label file are true negatives (24 of 300).
	- Boxes per class: mason_jar 182, apothecary_jar 194, spice_jar 186, hex_jar 203, squat_jam_jar 190
	- `meta/` holds per-object attribute JSON (class, box, visibility and module-specific attributes).
	- `data.yaml` included - train directly with Ultralytics YOLO.
	- Box size distribution (fraction of image width): median 0.074, 4% 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.