quailrobot/colors-v1-sample

QM Synthetic Colour Recognition (12 colours) - Free Sample

Buy the full commercial edition: $19 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 everyday objects in 12 named colours with YOLO bounding boxes where the class is the colour, plus exact sRGB values per object, for colour recognition, colour constancy and sorting robots; the paid full edition can be used commercially.

• Best for: colour-recognition models, colour-constancy research, sorting-robot prototypes
• Not for: calibrated colour measurement (use a colorimeter)

Quick start

# pip install huggingface_hub ultralytics
from huggingface_hub import snapshot_download
path = snapshot_download("quailrobot/colors-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 / colour classification (YOLO format) + exact sRGB regression target
• Labels: 12 classes: red, orange, yellow, green, teal, blue, purple, pink, brown, black, white, grey; exact sRGB per object in meta/
• Full edition size: 3,000 images 640x640 (train 2,386 / val 308 / test 306) with YOLO labels + attribute JSON (zip 60.8 MB)
• Free sample size: 300 images (train 223 / val 38 / test 39)
• 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: $19 USD; checkout may display the equivalent in your local currency
• Buy URL (primary): https://buy.polar.sh/polar_cl_UiPSyF3dOiIKHeQ5vR7he5LURWPu04RO3Cenc3RE8cI
• 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: $19 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_UiPSyF3dOiIKHeQ5vR7he5LURWPu04RO3Cenc3RE8cI
• 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

• Colour names follow our HSV ranges - boundaries between neighbouring names are a design choice.
• Simple object shapes, no coloured background clutter.

This free sample: 300 images (train 223 / val 38 / test 39). Full commercial edition: 3,000 images 640x640 (train 2,386 / val 308 / test 306) with YOLO labels + attribute JSON (zip 60.8 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.

Contents

split	images	boxes
train	223	963
val	38	138
test	39	149

• Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = red, 1 = orange, 2 = yellow, 3 = green, 4 = teal, 5 = blue, 6 = purple, 7 = pink, 8 = brown, 9 = black, 10 = white, 11 = grey. Images with an empty label file are true negatives (10 of 300).
• Boxes per class: red 104, orange 108, yellow 105, green 123, teal 108, blue 94, purple 107, pink 95, brown 95, black 108, white 90, grey 113
• 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 0.091, 1% 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.