Add operator YOLO training dataset tooling

backend/README.md

@@ -278,6 +278,38 @@ python scripts/configure_yolo_model.py \
 The smoke loads only the supplied local model file, does not run inference and
 does not download weights.
 
+Operator-only local training preparation is available when real public model
+candidates are too weak for the target imagery. It is not a browser feature and
+does not change API contracts:
+
+```bash
+docker exec -it geointel python /app/scripts/export_operator_yolo_dataset.py \
+  --manifest-path /app/storage/operator-data/operator_samples_manifest.json \
+  --output-dir /app/storage/operator-data/yolo-building-dataset \
+  --val-samples turnhout \
+  --force
+```
+
+In an AI-enabled runtime with an existing local base model:
+
+```bash
+docker exec \
+  -e OPERATOR_YOLO_DATASET_DIR=/app/storage/operator-data/yolo-building-dataset \
+  -e YOLO_BASE_MODEL_PATH=/app/models/yolov8n.pt \
+  -e TRAIN_MODEL_OUTPUT_PATH=/app/models/geointel-building-detector.pt \
+  -e TRAIN_EPOCHS=8 \
+  -e TRAIN_IMGSZ=512 \
+  -e TRAIN_BATCH=2 \
+  -e TRAIN_WORKERS=0 \
+  -e TRAIN_DEVICE=cpu \
+  geointel bash /app/scripts/train_operator_yolo_detector.sh
+```
+
+The exporter creates a YOLO `dataset.yaml` plus image/label folders from the
+explicit operator sample manifest. The training wrapper writes
+`training_summary.json` and a local `.pt` artifact, which still must be
+validated through model preflight and the real-data QA matrix before use.
+
 The backend also exposes a read-only model asset catalog for the mounted model
 directory: