Fix YOLO class normalization

backend/README.md

@@ -367,8 +367,10 @@ dataset through the normal dataset service, generates raster tiles, selects a
 local model asset, runs configured YOLO detection, compares persisted
 detections against persisted `vector_features`, persists QA/QC rows and exports
 the detection GeoJSON. It never seeds demo detections, enables fixture mode,
-fetches live providers or downloads model weights. Current V1 upload support is
-limited to GeoTIFF-style rasters and GeoJSON/JSON reference vectors.
+fetches live providers or downloads model weights. Configured-YOLO model class
+labels are normalized to lowercase for filtering and persisted detections while
+the original model label is retained in detection provenance. Current V1 upload
+support is limited to GeoTIFF-style rasters and GeoJSON/JSON reference vectors.
 
 ### Run backend
 

backend/app/services/detection_service.py

@@ -541,13 +541,14 @@ class DetectionService:
         model_path = Path(settings.yolo_model_path or "").expanduser()
         adapter = yolo_adapter_class(settings)
         model = adapter.load_model(model_path)
-        allowed_classes = set(class_filter)
+        allowed_classes = {DetectionService._canonical_class_name(value) for value in class_filter if DetectionService._canonical_class_name(value)}
         persisted: list[Detection] = []
         manifest_crs = manifest.get("crs") or manifest.get("source_crs") or manifest.get("dataset_crs") or "EPSG:4326"
         for tile in manifest["tiles"]:
             tile_path = DetectionService._resolve_tile_path(tile, Path(tile_manifest_path or "").expanduser())
             for raw in adapter.predict_tile(model, tile_path, confidence_threshold):
-                class_name = str(raw.get("class_name") or "")
+                model_class_name = str(raw.get("class_name") or "").strip()
+                class_name = DetectionService._canonical_class_name(model_class_name)
                 confidence = float(raw.get("confidence", 0.0))
                 if allowed_classes and class_name not in allowed_classes:
                     continue
@@ -557,6 +558,9 @@ class DetectionService:
                 if not isinstance(bbox, list):
                     raise AppError(code="DETECTION_INVALID_BBOX", message="YOLO adapter returned a detection without bbox", status_code=422)
                 geometry = pixel_bbox_to_epsg4326_polygon(bbox=bbox, tile=tile, crs=tile.get("crs") or manifest_crs)
+                properties = dict(raw.get("properties") or {})
+                if model_class_name and model_class_name != class_name:
+                    properties.setdefault("model_class_name", model_class_name)
                 detection = Detection(
                     id=uuid.uuid4(),
                     project_id=project_id,
@@ -575,7 +579,7 @@ class DetectionService:
                         "y_max": float(bbox[3]),
                     },
                     source_tile_path=str(tile_path),
-                    properties_json={**dict(raw.get("properties") or {}), "tile_index": tile.get("index")},
+                    properties_json={**properties, "tile_index": tile.get("index")},
                 )
                 db.add(detection)
                 persisted.append(detection)
@@ -584,6 +588,10 @@ class DetectionService:
             db.refresh(detection)
         return persisted
 
+    @staticmethod
+    def _canonical_class_name(value: Any) -> str:
+        return str(value or "").strip().casefold()
+
     @staticmethod
     def _load_tile_manifest(tile_manifest_path: str | None, max_tiles: int) -> dict[str, Any]:
         if not tile_manifest_path:

backend/tests/test_sprint8b_yolo_foundation.py

@@ -77,6 +77,18 @@ class MockYoloAdapter:
         ]
 
 
+class MixedCaseYoloAdapter(MockYoloAdapter):
+    def predict_tile(self, model, tile_path: Path, confidence_threshold: float) -> list[dict]:
+        return [
+            {
+                "class_name": "Building",
+                "confidence": 0.91,
+                "bbox": [10.0, 20.0, 30.0, 40.0],
+                "properties": {"adapter": "mock"},
+            }
+        ]
+
+
 class RecordingPredictModel:
     def __init__(self) -> None:
         self.seen_sources: list[dict] = []
@@ -343,6 +355,33 @@ def test_yolo_run_persists_mocked_georeferenced_detections(tmp_path: Path) -> No
     assert jobs[0].status == "success"
 
 
+def test_yolo_class_filter_is_case_insensitive_and_persists_canonical_class(tmp_path: Path) -> None:
+    db, project_id, dataset_id = _project_and_dataset()
+    model_path = tmp_path / "model.pt"
+    model_path.write_bytes(b"local weights")
+    settings = _settings(tmp_path, yolo_model_path=str(model_path))
+    manifest_path = _manifest(tmp_path, tile_count=1)
+
+    result = DetectionService.run_detection(
+        db=db,
+        project_id=project_id,
+        dataset_id=dataset_id,
+        model_id="yolo-configured",
+        confidence_threshold=0.5,
+        class_filter=["building"],
+        tile_manifest_path=str(manifest_path),
+        settings=settings,
+        yolo_adapter_class=MixedCaseYoloAdapter,
+    )
+
+    detections = [item for item in db.added if isinstance(item, Detection)]
+
+    assert result.status == "success"
+    assert result.detection_count == 1
+    assert detections[0].class_name == "building"
+    assert detections[0].properties_json["model_class_name"] == "Building"
+
+
 def test_yolo_adapter_converts_single_band_tiles_to_rgb_before_prediction(tmp_path: Path) -> None:
     Image = pytest.importorskip("PIL.Image")
     tile_path = tmp_path / "single_band_tile.tif"