Datasets:

quailrobot
/

fruit-v1-sample

license: cc-by-nc-sa-4.0
task_categories:
  - object-detection
tags:
  - fruit
  - ripeness
  - agriculture
  - food
  - synthetic
  - yolo
pretty_name: QM Synthetic Fruit with Ripeness Labels (6 fruits)
size_categories:
  - n<1K

QM Synthetic Fruit with Ripeness Labels (6 fruits) - Free Sample

Buy the full commercial edition: $5 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 Object-Detection Dataset of Your Object (2,000 images), $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 fruits with YOLO bounding boxes and ripeness labels (unripe / ripe / overripe), for agritech sorting, retail shelf monitoring and robotic picking; the paid full edition can be used commercially.

Best for: fruit detection and ripeness pre-training, sorting-line prototypes
Not for: food-safety or quality decisions without real-photo validation

Quick start

# pip install huggingface_hub ultralytics
from huggingface_hub import snapshot_download
path = snapshot_download("quailrobot/fruit-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) + ripeness attribute
Labels: 6 classes: 0 apple, 1 orange, 2 lemon, 3 banana, 4 tomato, 5 pear; ripeness stage + blemish count in meta/
Full edition size: 5,000 images 640x640 (train 3,977 / val 520 / test 503) with YOLO labels + attribute JSON (zip 112.0 MB)
Free sample size: 300 images (train 247 / val 31 / test 22)
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_I15FSJ1fslvMqru1TMDXqoDTS1eIHgGL90lBK2RtCMN
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_I15FSJ1fslvMqru1TMDXqoDTS1eIHgGL90lBK2RtCMN
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

Clean, semi-stylized renders - fine-tune on real photos for production.
Ripeness is modelled through skin colour and blemishes only.

This free sample: 300 images (train 247 / val 31 / test 22). Full commercial edition: 5,000 images 640x640 (train 3,977 / val 520 / test 503) with YOLO labels + attribute JSON (zip 112.0 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.

split	images	boxes
train	247	1298
val	31	178
test	22	130

Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = apple, 1 = orange, 2 = lemon, 3 = banana, 4 = tomato, 5 = pear. Images with an empty label file are true negatives (3 of 300).
Boxes per class: apple 259, orange 285, lemon 280, banana 205, tomato 280, pear 297
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 0.117, 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.