quailrobot/drone-v5-sample

QM Photoreal Drone Detection v5 (real-data validated) - Free Sample

Buy the full commercial edition: $39 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 synthetic dataset, $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

Photoreal synthetic images of drones (consumer, folding, FPV and heavy-lift designs) rendered into real-world HDRI environments, with YOLO bounding boxes, for pre-training drone detectors; the paid full edition can be used commercially.

• Best for: pre-training single-class drone / counter-UAS detectors before fine-tuning on a small real set
• Not for: deploying a detector trained on synthetic images only; fixed-wing or thermal detection

Quick start

# pip install huggingface_hub ultralytics
from huggingface_hub import snapshot_download
path = snapshot_download("quailrobot/drone-v5-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), single class
• Labels: 1 class: 0 drone; birds appear unlabelled; empty label file = true negative (736 of 6,000 images in the full edition, 31 of 300 in the sample)
• Full edition size: 6,000 images 640x640 (train 4,872 / val 565 / test 563) with 6,595 drone boxes (zip 209.7 MB)
• Free sample size: 300 images (train 243 / val 36 / test 21) with 336 drone boxes
• Format: 640x640 JPEG images + YOLO txt labels + data.yaml (Ultralytics-ready) + per-object attribute JSON
• 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: $39 USD; checkout may display the equivalent in your local currency
• Buy URL (primary): https://buy.polar.sh/polar_cl_1QltnCWa2soRLKlNTPmUkFMpV9LAXZMRVpvZW1OVvz4
• Licence (full edition): LicenseRef-QuailModel-Commercial (commercial use allowed, no resale of the data)
• Licence (free sample): CC-BY-NC-SA-4.0
• Validation: Sim-to-real test (YOLOv8n, 1,000 held-out real drone photos from the Seraphim dataset, CC BY 4.0, evaluation only), mean of 3 training seeds: pre-training on Drone v5 then fine-tuning on 200 real images = mAP50-95 0.407 vs 0.367 for the same 200 real images alone (+4.0 points, +11%; all 3 seeds above all 3 baseline seeds) and mAP50 0.749 vs 0.719 (seed std 0.009 vs 0.036). Drone v5 alone, no real images (1 seed): mAP50 0.410 / mAP50-95 0.20. Caveat: the shot-type mix was tuned using the box-size statistics of the real test set, so treat the result as slightly optimistic; a re-test on an independent real test set is in progress.
• Data source: Synthetic: original procedural drone models rendered with Blender/Cycles into 31 real-world CC0 HDRI environments from Poly Haven; generator code written with an AI model (AI-assisted)
• Catalog (all QuailModel datasets, catalog.json, llms.txt): https://quailrobot-quailmodel.static.hf.space
• Last updated: 2026-10-10

Validation

Sim-to-real test (YOLOv8n, 1,000 held-out real drone photos from the Seraphim dataset, CC BY 4.0, evaluation only), mean of 3 training seeds: pre-training on Drone v5 then fine-tuning on 200 real images = mAP50-95 0.407 vs 0.367 for the same 200 real images alone (+4.0 points, +11%; all 3 seeds above all 3 baseline seeds) and mAP50 0.749 vs 0.719 (seed std 0.009 vs 0.036). Drone v5 alone, no real images (1 seed): mAP50 0.410 / mAP50-95 0.20. Caveat: the shot-type mix was tuned using the box-size statistics of the real test set, so treat the result as slightly optimistic; a re-test on an independent real test set is in progress.

Price & licence

• Full edition: $39 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_1QltnCWa2soRLKlNTPmUkFMpV9LAXZMRVpvZW1OVvz4
• 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 alone does not replace real data (mAP50 0.410 with no real images): pre-train on this, then fine-tune on real images.
• Validation is slightly optimistic: the shot-type mix was tuned on real test-set box-size statistics (independent re-test in progress).
• Single class (drone); no fixed-wing aircraft class (see Airspace v3) and no thermal imagery (see Thermal v4).
• Small objects: 8% of sample boxes are narrower than 16 px; drone designs are original procedural models, not specific brands.

This free sample: 300 images (train 243 / val 36 / test 21) with 336 drone boxes. Full commercial edition: 6,000 images 640x640 (train 4,872 / val 565 / test 563) with 6,595 drone boxes (zip 209.7 MB). Fully synthetic data; summary, facts, validation and limitations are in the block above.

Contents

split	images	boxes
train	243	267
val	36	38
test	21	31

• Image size: 640x640 JPEG. Labels: YOLO txt, classes: 0 = drone. Images with an empty label file are true negatives (31 of 300).
• Boxes per class: drone 336
• 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 0.142, 8% 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. Real-photo evaluation set: Seraphim drone dataset (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/), used for evaluation only and not redistributed.

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.