QuailModel sample: QM Synthetic Glass Jars (5 types + attributes)

README.md

@@ -15,74 +15,8 @@ size_categories:
 ---
 # QM Synthetic Glass Jars (5 types + attributes) - Free Sample
 
-<!-- qm-buy:start -->
-**Buy the full commercial edition: $25 USD -> [Polar checkout, instant download](https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej)**  Also on [Gumroad](https://quailcraft1.gumroad.com/l/synthetic-glass-jars).  
-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](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 photos of glass jars (5 jar types) with YOLO bounding boxes and per-object attributes (tint, lid, contents, fill level), for retail shelf, kitchen robotics and inventory models; the paid full edition can be used commercially.
-- Best for: jar detection / attribute pre-training, robot grasping research, inventory demos
-- Not for: recognising specific commercial brands or labels
-
-## Quick start
-```python
-# pip install huggingface_hub ultralytics
-from huggingface_hub import snapshot_download
-path = snapshot_download("quailrobot/jars-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: 5 classes: 0 mason_jar, 1 apothecary_jar, 2 spice_jar, 3 hex_jar, 4 squat_jam_jar; attributes in meta/ (tint, lid colour/type, contents, fill level, height)
-- Full edition size: 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB)
-- Free sample size: 300 images (train 237 / val 34 / test 29)
-- 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): https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej
-- Buy URL (secondary, also on Gumroad): https://quailcraft1.gumroad.com/l/synthetic-glass-jars
-- 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: $25 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_7qTni7LxfUbHsqVdcX93IKNhEZa9ieFHwHxWq36r8ej (also on Gumroad: https://quailcraft1.gumroad.com/l/synthetic-glass-jars)
-- 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
-- Synthetic renders (Cycles path tracing): fine-tune on real photos before production.
-- Five original jar families only - not brand-specific products.
-<!-- qm-buy:end -->
-
-Synthetic photos of glass jars (5 jar types) with YOLO bounding boxes and per-object attributes (tint, lid, contents, fill level), for retail shelf, kitchen robotics and inventory models; the paid full edition can be used commercially.
-
-## What's inside
-- **This free sample:** 300 images (train 237 / val 34 / test 29)
-- **Full commercial edition:** 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB)
-- **Task:** object detection (YOLO format) + attribute labels
-- **Labels:** 5 classes: 0 mason_jar, 1 apothecary_jar, 2 spice_jar, 3 hex_jar, 4 squat_jam_jar; attributes in meta/ (tint, lid colour/type, contents, fill level, height)
-- **Format:** 640x640 JPEG images + YOLO txt labels + data.yaml (Ultralytics-ready)
+**This free sample:** 300 images (train 237 / val 34 / test 29). **Full commercial edition:** 5,000 images 640x640 (train 3,975 / val 529 / test 496) with YOLO labels + attribute JSON (zip 98.4 MB).
+Fully synthetic data; summary, facts, validation and limitations are in the block above.
 
 ## Contents
 | split | images | boxes |
@@ -100,16 +34,6 @@ Synthetic photos of glass jars (5 jar types) with YOLO bounding boxes and per-ob
 ## 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).
 
-## Validation
-Labels are computed from exact projected 3D mesh vertices (no hand labelling). No benchmark on real photos has been published for this dataset yet.
-
-## Limitations
-- Synthetic renders : fine-tune on real photos before production.
-- Five original jar families only - not brand-specific products.
-
-## Best for / not for
-Best for: jar detection / attribute pre-training, robot grasping research, inventory demos. Not for: recognising specific commercial brands or labels.
-
 ## Credits
 Lighting environments: Poly Haven HDRIs (CC0), credited.