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

README.md

@@ -44,12 +44,12 @@ YOLO("yolov8n.pt").train(data=f"{path}/data.yaml", epochs=50, imgsz=640)
 - Buy URL (primary): https://buy.polar.sh/polar_cl_NG5kSyz9EzZm7OKoEKYG2AJSnHJFFyX8HQU2X3tdym8
 - 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, single seed, 1,000 held-out real drone photos from the Seraphim dataset): 200 real images alone mAP50 0.678; QuailModel synthetic (Drone v2 + Airspace v3 combined) pre-training then fine-tune on the same 200 real images mAP50 0.747; synthetic only 0.260; 2,000 real images 0.807.
+- Validation: Sim-to-real test (YOLOv8n, 1,000 held-out real drone photos from the Seraphim dataset, CC BY 4.0, evaluation only): 200 real images alone = mAP50 0.719 / mAP50-95 0.367 (mean of 3 seeds; range 0.678-0.744 / 0.337-0.384). Pre-training on QuailModel synthetic (Drone v2 + Airspace v3) then fine-tuning on the same 200 real images = mAP50 0.747 / mAP50-95 0.395 (1 seed): the mAP50 gain is within seed noise; mAP50-95 is +2.8 points. Our newer photoreal Drone v5 gives a consistent +4.0 points mAP50-95 (+11%) across 3 seeds and lower variance. 2,000 real images: 0.807 / 0.491. Synthetic data does not replace real data - use it to pre-train.
 - Data source: 100% synthetic, generated by QuailModel with AI assistance (generator code written with an AI model)
 - Last updated: 2026-10-10
 
 ## Validation
-Sim-to-real test (YOLOv8n, single seed, 1,000 held-out real drone photos from the Seraphim dataset): 200 real images alone mAP50 0.678; QuailModel synthetic (Drone v2 + Airspace v3 combined) pre-training then fine-tune on the same 200 real images mAP50 0.747; synthetic only 0.260; 2,000 real images 0.807.
+Sim-to-real test (YOLOv8n, 1,000 held-out real drone photos from the Seraphim dataset, CC BY 4.0, evaluation only): 200 real images alone = mAP50 0.719 / mAP50-95 0.367 (mean of 3 seeds; range 0.678-0.744 / 0.337-0.384). Pre-training on QuailModel synthetic (Drone v2 + Airspace v3) then fine-tuning on the same 200 real images = mAP50 0.747 / mAP50-95 0.395 (1 seed): the mAP50 gain is within seed noise; mAP50-95 is +2.8 points. Our newer photoreal Drone v5 gives a consistent +4.0 points mAP50-95 (+11%) across 3 seeds and lower variance. 2,000 real images: 0.807 / 0.491. Synthetic data does not replace real data - use it to pre-train.
 
 ## Price & licence
 - Full edition: $35 USD. One-time payment, instant download after checkout: https://buy.polar.sh/polar_cl_NG5kSyz9EzZm7OKoEKYG2AJSnHJFFyX8HQU2X3tdym8