Record unique hard-negative YOLO candidate gate

docs/TODO.md

@@ -108,6 +108,7 @@ This file now starts with the current implementation status. Older preparation/b
 - [x] Add a hard-negative model-quality pass with sparse/background AOIs and explicit false-positive scoring.
 - [x] Train a hard-negative-balanced YOLO candidate and rerun dense QA plus background false-positive matrices.
 - [x] Benchmark an external remote-sensing YOLOv8l building candidate as an explicit local model asset.
+- [x] Train and gate the `uniquehardneg160e50` YOLOv8s candidate through 7 positive AOIs and 9 hard-negative/background samples.
 - [ ] Find or train a materially stronger aerial/Kempen building model candidate; `geointel-building-yolov8n-expanded160e50-pt` is the best current dense-AOI candidate but still too weak and too noisy for a V1 default.
 - [ ] Train a higher-capacity local aerial-building detector with stronger positive recall while preserving the hard-negative false-positive gate.