Bump brace-expansion in /docs

Bumps  and [brace-expansion](https://github.com/juliangruber/brace-expansion). These dependencies needed to be updated together.

Updates `brace-expansion` from 1.1.12 to 1.1.13
- [Release notes](https://github.com/juliangruber/brace-expansion/releases)
- [Commits](https://github.com/juliangruber/brace-expansion/compare/v1.1.12...v1.1.13)

Updates `brace-expansion` from 2.0.2 to 2.0.3
- [Release notes](https://github.com/juliangruber/brace-expansion/releases)
- [Commits](https://github.com/juliangruber/brace-expansion/compare/v1.1.12...v1.1.13)

---
updated-dependencies:
- dependency-name: brace-expansion
  dependency-version: 1.1.13
  dependency-type: indirect
- dependency-name: brace-expansion
  dependency-version: 2.0.3
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>

docs/package-lock.json

@@ -6820,9 +6820,9 @@
       }
     },
     "node_modules/brace-expansion": {
-      "version": "2.0.2",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.2.tgz",
-      "integrity": "sha512-Jt0vHyM+jmUBqojB7E1NIYadt0vI0Qxjxd2TErW94wDz+E2LAm5vKMXXwg6ZZBTHPuUlDgQHKXvjGBdfcF1ZDQ==",
+      "version": "2.0.3",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-2.0.3.tgz",
+      "integrity": "sha512-MCV/fYJEbqx68aE58kv2cA/kiky1G8vux3OR6/jbS+jIMe/6fJWa0DTzJU7dqijOWYwHi1t29FlfYI9uytqlpA==",
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0"
@@ -11471,9 +11471,9 @@
       "license": "BSD-2-Clause"
     },
     "node_modules/glob/node_modules/brace-expansion": {
-      "version": "1.1.12",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
-      "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
+      "version": "1.1.13",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.13.tgz",
+      "integrity": "sha512-9ZLprWS6EENmhEOpjCYW2c8VkmOvckIJZfkr7rBW6dObmfgJ/L1GpSYW5Hpo9lDz4D1+n0Ckz8rU7FwHDQiG/w==",
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0",
@@ -20609,9 +20609,9 @@
       }
     },
     "node_modules/serve-handler/node_modules/brace-expansion": {
-      "version": "1.1.12",
-      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.12.tgz",
-      "integrity": "sha512-9T9UjW3r0UW5c1Q7GTwllptXwhvYmEzFhzMfZ9H7FQWt+uZePjZPjBP/W1ZEyZ1twGWom5/56TF4lPcqjnDHcg==",
+      "version": "1.1.13",
+      "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.13.tgz",
+      "integrity": "sha512-9ZLprWS6EENmhEOpjCYW2c8VkmOvckIJZfkr7rBW6dObmfgJ/L1GpSYW5Hpo9lDz4D1+n0Ckz8rU7FwHDQiG/w==",
       "license": "MIT",
       "dependencies": {
         "balanced-match": "^1.0.0",

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(