--- 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](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. **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.