quailrobot/plants-v1-sample

QM Synthetic Potted Plants (6 types, stylized) - 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

Stylized synthetic images of 6 potted plant types with YOLO bounding boxes and pot/leaf attributes, for pre-training, robotics pick-and-place and plant-type classification research; the paid full edition can be used commercially.

• Best for: pre-training and robotics pick-and-place prototypes
• Not for: plant species identification in real photos without fine-tuning

Quick start

# pip install huggingface_hub ultralytics
from huggingface_hub import snapshot_download
path = snapshot_download("quailrobot/plants-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 echeveria_rosette, 1 barrel_cactus, 2 column_cactus, 3 snake_plant, 4 pilea_round_leaf, 5 aloe_spiky; pot style, pot diameter, leaf/rib count in meta/
• Full edition size: 5,000 images 640x640 (train 4,009 / val 482 / test 509) with YOLO labels + attribute JSON (zip 100.5 MB)
• Free sample size: 300 images (train 235 / val 32 / test 33)
• 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_e1JErFESn1qSLPknUg58BGGv9FksAsRuRqz9L2mSU5U
• 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_e1JErFESn1qSLPknUg58BGGv9FksAsRuRqz9L2mSU5U
• 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

• Not photorealistic (low-poly, stylized) - pair with real photos for fine-tuning.
• Six plant families only.

This free sample: 300 images (train 235 / val 32 / test 33). Full commercial edition: 5,000 images 640x640 (train 4,009 / val 482 / test 509) with YOLO labels + attribute JSON (zip 100.5 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.

Contents

split	images	boxes
train	235	586
val	32	65
test	33	85

• Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = echeveria_rosette, 1 = barrel_cactus, 2 = column_cactus, 3 = snake_plant, 4 = pilea_round_leaf, 5 = aloe_spiky. Images with an empty label file are true negatives (16 of 300).
• Boxes per class: echeveria_rosette 126, barrel_cactus 127, column_cactus 110, snake_plant 113, pilea_round_leaf 127, aloe_spiky 133
• 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 0.115, 0% 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.