Polar checkout as primary buy link + agent-first facts block

README.md

@@ -9,14 +9,58 @@ tags:
 - food
 - synthetic
 - yolo
-pretty_name: "QM Synthetic Fruit with Ripeness Labels (6 fruits)"
+pretty_name: QM Synthetic Fruit with Ripeness Labels (6 fruits)
 size_categories:
 - n<1K
 ---
 # QM Synthetic Fruit with Ripeness Labels (6 fruits) - Free Sample
 
-> **Full commercial edition: 5,000 images - $25 -> [buy on Gumroad](https://quailcraft1.gumroad.com/l/synthetic-fruit-ripeness)**  
-> This free sample is non-commercial (CC BY-NC-SA 4.0). Custom sizes on request.
+<!-- qm-buy:start -->
+**Buy the full commercial edition: $25 USD -> [Polar checkout, instant download](https://buy.polar.sh/polar_cl_I15FSJ1fslvMqru1TMDXqoDTS1eIHgGL90lBK2RtCMN)** Secondary store: [Gumroad](https://quailcraft1.gumroad.com/l/synthetic-fruit-ripeness)  
+This free sample is non-commercial (CC BY-NC-SA 4.0). The paid full edition has a commercial licence.
+
+## 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: $25 USD; checkout may display the equivalent in your local currency
+- Buy URL (primary, automated checkout): https://buy.polar.sh/polar_cl_I15FSJ1fslvMqru1TMDXqoDTS1eIHgGL90lBK2RtCMN
+- Buy URL (secondary): https://quailcraft1.gumroad.com/l/synthetic-fruit-ripeness
+- 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)
+- 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: $25 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 -->
 
 Synthetic **fruit + ripeness** images: apples, oranges, lemons, bananas, tomatoes and pears with pixel-exact YOLO
 boxes, and every fruit labelled with a **ripeness stage** (unripe / ripe / overripe, driving skin colour) and