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

<!-- qm-buy:start -->
**Buy the full commercial edition: $5 USD -> [Polar checkout, instant download](https://buy.polar.sh/polar_cl_I15FSJ1fslvMqru1TMDXqoDTS1eIHgGL90lBK2RtCMN)**  
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 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
```python
# 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.
<!-- qm-buy:end -->

**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.

## Contents
| 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.

	---
	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

	<!-- qm-buy:start -->
	Buy the full commercial edition: $5 USD -> [Polar checkout, instant download](https://buy.polar.sh/polar_cl_I15FSJ1fslvMqru1TMDXqoDTS1eIHgGL90lBK2RtCMN)
	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 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
	```python
	# 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.
	<!-- qm-buy:end -->

	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.

	## Contents
	\| 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.