frigate/detectors/plugins/onnx.py

@@ -8,8 +8,6 @@ from frigate.detectors.detection_api import DetectionApi
 from frigate.detectors.detection_runners import get_optimized_runner
 from frigate.detectors.detector_config import (
     BaseDetectorConfig,
-    InputDTypeEnum,
-    InputTensorEnum,
     ModelTypeEnum,
 )
 from frigate.util.model import (
@@ -61,34 +59,8 @@ class ONNXDetector(DetectionApi):
         if self.onnx_model_type == ModelTypeEnum.yolox:
             self.calculate_grids_strides()
 
-        self._warmup(detector_config)
         logger.info(f"ONNX: {path} loaded")
 
-    def _warmup(self, detector_config: ONNXDetectorConfig) -> None:
-        """Run a warmup inference to front-load one-time compilation costs.
-
-        Some GPU backends have a slow first inference: CUDA may need PTX JIT
-        compilation on newer architectures (e.g. NVIDIA 50-series / Blackwell),
-        and MIGraphX compiles the model graph on first run. Running it here
-        (during detector creation) keeps the watchdog start_time at 0.0 so the
-        process won't be killed.
-        """
-        if detector_config.model.input_tensor == InputTensorEnum.nchw:
-            shape = (1, 3, detector_config.model.height, detector_config.model.width)
-        else:
-            shape = (1, detector_config.model.height, detector_config.model.width, 3)
-
-        if detector_config.model.input_dtype in (
-            InputDTypeEnum.float,
-            InputDTypeEnum.float_denorm,
-        ):
-            dtype = np.float32
-        else:
-            dtype = np.uint8
-
-        logger.info("ONNX: warming up detector (may take a while on first run)...")
-        self.detect_raw(np.zeros(shape, dtype=dtype))
-
     def detect_raw(self, tensor_input: np.ndarray):
         if self.onnx_model_type == ModelTypeEnum.dfine:
             tensor_output = self.runner.run(

web/src/views/classification/ModelTrainingView.tsx

@@ -601,9 +601,7 @@ function LibrarySelector({
   const [confirmDelete, setConfirmDelete] = useState<string | null>(null);
   const [renameClass, setRenameClass] = useState<string | null>(null);
   const pageTitle = useMemo(() => {
-    if (pageToggle == "none") {
+    if (pageToggle != "train") {
-      return t("details.none");
-    } else if (pageToggle != "train") {
       return pageToggle;
     }