docs/docs/configuration/license_plate_recognition.md

@@ -184,7 +184,7 @@ cameras:
     ffmpeg: ... # add your streams
     detect:
       enabled: True
-      fps: 5 # increase to 10 if vehicles move quickly across your frame. Higher than 15 is unnecessary and is not recommended.
+      fps: 5 # increase to 10 if vehicles move quickly across your frame. Higher than 10 is unnecessary and is not recommended.
       min_initialized: 2
       width: 1920
       height: 1080
@@ -267,7 +267,7 @@ With this setup:
 - Review items will always be classified as a `detection`.
 - Snapshots will always be saved.
 - Zones and object masks are **not** used.
-- The `frigate/events` MQTT topic will **not** publish tracked object updates with the license plate bounding box and score, though `frigate/reviews` will publish if recordings are enabled. If a plate is recognized as a known plate, publishing will occur with an updated `sub_label` field. If characters are recognized, publishing will occur with an updated `recognized_license_plate` field.
+- The `frigate/events` MQTT topic will **not** publish tracked object updates, though `frigate/reviews` will if recordings are enabled.
 - License plate snapshots are saved at the highest-scoring moment and appear in Explore.
 - Debug view will not show `license_plate` bounding boxes.
 
@@ -280,7 +280,7 @@ With this setup:
 | Object Detection        | Standard Frigate+ detection applies                    | Bypasses standard object detection                              |
 | Zones & Object Masks    | Supported                                              | Not supported                                                   |
 | Debug View              | May show `license_plate` bounding boxes                | May **not** show `license_plate` bounding boxes                 |
-| MQTT `frigate/events`   | Publishes tracked object updates                       | Publishes limited updates                                       |
+| MQTT `frigate/events`   | Publishes tracked object updates                       | Does **not** publish tracked object updates                     |
 | Explore                 | Recognized plates available in More Filters            | Recognized plates available in More Filters                     |
 
 By selecting the appropriate configuration, users can optimize their dedicated LPR cameras based on whether they are using a Frigate+ model or the secondary LPR pipeline.

docs/docs/configuration/object_detectors.md

@@ -659,7 +659,7 @@ YOLOv3, YOLOv4, YOLOv7, and [YOLOv9](https://github.com/WongKinYiu/yolov9) model
 
 :::tip
 
-The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well.  See [the models section](#downloading-yolo-models) for more information on downloading YOLO models for use in Frigate.
+The YOLO detector has been designed to support YOLOv3, YOLOv4, YOLOv7, and YOLOv9 models, but may support other YOLO model architectures as well.
 
 :::
 
@@ -682,29 +682,6 @@ model:
 
 Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
 
-#### YOLOx
-
-[YOLOx](https://github.com/Megvii-BaseDetection/YOLOX) models are supported, but not included by default. See [the models section](#downloading-yolo-models) for more information on downloading the YOLOx model for use in Frigate.
-
-After placing the downloaded onnx model in your config folder, you can use the following configuration:
-
-```yaml
-detectors:
-  onnx:
-    type: onnx
-
-model:
-  model_type: yolox
-  width: 416 # <--- should match the imgsize set during model export
-  height: 416 # <--- should match the imgsize set during model export
-  input_tensor: nchw_denorm
-  input_dtype: float
-  path: /config/model_cache/yolox_tiny.onnx
-  labelmap_path: /labelmap/coco-80.txt
-```
-
-Note that the labelmap uses a subset of the complete COCO label set that has only 80 objects.
-
 #### RF-DETR
 
 [RF-DETR](https://github.com/roboflow/rf-detr) is a DETR based model. The ONNX exported models are supported, but not included by default. See [the models section](#downloading-rf-detr-model) for more information on downloading the RF-DETR model for use in Frigate.
@@ -985,10 +962,6 @@ The input image size in this notebook is set to 320x320. This results in lower C
 
 ### Downloading YOLO Models
 
-#### YOLOx
-
-YOLOx models can be downloaded [from the YOLOx repo](https://github.com/Megvii-BaseDetection/YOLOX/tree/main/demo/ONNXRuntime).
-
 #### YOLOv3, YOLOv4, and YOLOv7
 
 To export as ONNX:

frigate/config/config.py

@@ -513,14 +513,10 @@ class FrigateConfig(FrigateBaseModel):
                     )
 
             # Warn if detect fps > 10
-            if camera_config.detect.fps > 10 and camera_config.type != "lpr":
+            if camera_config.detect.fps > 10:
                 logger.warning(
                     f"{camera_config.name} detect fps is set to {camera_config.detect.fps}. This does NOT need to match your camera's frame rate. High values could lead to reduced performance. Recommended value is 5."
                 )
-            if camera_config.detect.fps > 15 and camera_config.type == "lpr":
-                logger.warning(
-                    f"{camera_config.name} detect fps is set to {camera_config.detect.fps}. This does NOT need to match your camera's frame rate. High values could lead to reduced performance. Recommended value for LPR cameras are between 5-15."
-                )
 
             # Default min_initialized configuration
             min_initialized = int(camera_config.detect.fps / 2)

frigate/data_processing/common/license_plate/mixin.py

@@ -490,6 +490,10 @@ class LicensePlateProcessingMixin:
                 merged_boxes.append(current_box)
                 current_box = next_box
 
+            logger.debug(
+                f"Provided plate_width: {plate_width}, max_gap: {max_gap}, horizontal_gap: {horizontal_gap}"
+            )
+
         # Add the last box
         merged_boxes.append(current_box)
 
@@ -1129,7 +1133,7 @@ class LicensePlateProcessingMixin:
         # 4. Log the comparison
         logger.debug(
             f"Plate comparison - Current: {top_plate} (score: {curr_score:.3f}, min_conf: {curr_min_conf:.2f}) vs "
-            f"Previous: {prev_plate} (score: {prev_score:.3f}, min_conf: {prev_min_conf:.2f}) "
+            f"Previous: {prev_plate} (score: {prev_score:.3f}, min_conf: {prev_min_conf:.2f})\n"
             f"Metrics - Length: {len(top_plate)} vs {len(prev_plate)} (scores: {curr_length_score:.2f} vs {prev_length_score:.2f}), "
             f"Area: {top_area} vs {prev_area}, "
             f"Avg Conf: {avg_confidence:.2f} vs {prev_avg_confidence:.2f}, "
@@ -1259,15 +1263,6 @@ class LicensePlateProcessingMixin:
                 )
                 return
 
-            # don't run for objects with no position changes
-            # this is the initial state after registering a new tracked object
-            # LPR will run 2 frames after detect.min_initialized is reached
-            if obj_data.get("position_changes", 0) == 0:
-                logger.debug(
-                    f"{camera}: Plate detected in {self.config.cameras[camera].detect.min_initialized + 1} concurrent frames, LPR frame threshold ({self.config.cameras[camera].detect.min_initialized + 2})"
-                )
-                return
-
             license_plate: Optional[dict[str, any]] = None
 
             if "license_plate" not in self.config.cameras[camera].objects.track:
@@ -1406,8 +1401,6 @@ class LicensePlateProcessingMixin:
                     license_plate_frame,
                 )
 
-        logger.debug(f"{camera}: Running plate recognition.")
-
         # run detection, returns results sorted by confidence, best first
         start = datetime.datetime.now().timestamp()
         license_plates, confidences, areas = self._process_license_plate(

frigate/data_processing/post/license_plate.py

@@ -54,9 +54,6 @@ class LicensePlatePostProcessor(LicensePlateProcessingMixin, PostProcessorApi):
         Returns:
             None.
         """
-        # don't run LPR post processing for now
-        return
-
         event_id = data["event_id"]
         camera_name = data["camera"]
 

frigate/detectors/detection_api.py

@@ -16,7 +16,7 @@ class DetectionApi(ABC):
     @abstractmethod
     def __init__(self, detector_config: BaseDetectorConfig):
         self.detector_config = detector_config
-        self.thresh = 0.4
+        self.thresh = 0.5
         self.height = detector_config.model.height
         self.width = detector_config.model.width
 
@@ -24,21 +24,58 @@ class DetectionApi(ABC):
     def detect_raw(self, tensor_input):
         pass
 
-    def calculate_grids_strides(self) -> None:
-        grids = []
-        expanded_strides = []
+    def post_process_yolonas(self, output):
+        """
+        @param output: output of inference
+        expected shape: [np.array(1, N, 4), np.array(1, N, 80)]
+        where N depends on the input size e.g. N=2100 for 320x320 images
 
-        # decode and orient predictions
-        strides = [8, 16, 32]
-        hsizes = [self.height // stride for stride in strides]
-        wsizes = [self.width // stride for stride in strides]
+        @return: best results: np.array(20, 6) where each row is
+        in this order (class_id, score, y1/height, x1/width, y2/height, x2/width)
+        """
 
-        for hsize, wsize, stride in zip(hsizes, wsizes, strides):
-            xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
-            grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
-            grids.append(grid)
-            shape = grid.shape[:2]
-            expanded_strides.append(np.full((*shape, 1), stride))
+        N = output[0].shape[1]
 
-        self.grids = np.concatenate(grids, 1)
-        self.expanded_strides = np.concatenate(expanded_strides, 1)
+        boxes = output[0].reshape(N, 4)
+        scores = output[1].reshape(N, 80)
+
+        class_ids = np.argmax(scores, axis=1)
+        scores = scores[np.arange(N), class_ids]
+
+        args_best = np.argwhere(scores > self.thresh)[:, 0]
+
+        num_matches = len(args_best)
+        if num_matches == 0:
+            return np.zeros((20, 6), np.float32)
+        elif num_matches > 20:
+            args_best20 = np.argpartition(scores[args_best], -20)[-20:]
+            args_best = args_best[args_best20]
+
+        boxes = boxes[args_best]
+        class_ids = class_ids[args_best]
+        scores = scores[args_best]
+
+        boxes = np.transpose(
+            np.vstack(
+                (
+                    boxes[:, 1] / self.height,
+                    boxes[:, 0] / self.width,
+                    boxes[:, 3] / self.height,
+                    boxes[:, 2] / self.width,
+                )
+            )
+        )
+
+        results = np.hstack(
+            (class_ids[..., np.newaxis], scores[..., np.newaxis], boxes)
+        )
+
+        return np.resize(results, (20, 6))
+
+    def post_process(self, output):
+        if self.detector_config.model.model_type == ModelTypeEnum.yolonas:
+            return self.post_process_yolonas(output)
+        else:
+            raise ValueError(
+                f'Model type "{self.detector_config.model.model_type}" is currently not supported.'
+            )

frigate/detectors/detector_config.py

@@ -31,7 +31,6 @@ class InputTensorEnum(str, Enum):
 
 class InputDTypeEnum(str, Enum):
     float = "float"
-    float_denorm = "float_denorm"  # non-normalized float
     int = "int"
 
 

frigate/detectors/plugins/onnx.py

@@ -14,7 +14,6 @@ from frigate.util.model import (
     post_process_dfine,
     post_process_rfdetr,
     post_process_yolo,
-    post_process_yolox,
 )
 
 logger = logging.getLogger(__name__)
@@ -31,8 +30,6 @@ class ONNXDetector(DetectionApi):
     type_key = DETECTOR_KEY
 
     def __init__(self, detector_config: ONNXDetectorConfig):
-        super().__init__(detector_config)
-
         try:
             import onnxruntime as ort
 
@@ -54,14 +51,13 @@ class ONNXDetector(DetectionApi):
             path, providers=providers, provider_options=options
         )
 
+        self.h = detector_config.model.height
+        self.w = detector_config.model.width
         self.onnx_model_type = detector_config.model.model_type
         self.onnx_model_px = detector_config.model.input_pixel_format
         self.onnx_model_shape = detector_config.model.input_tensor
         path = detector_config.model.path
 
-        if self.onnx_model_type == ModelTypeEnum.yolox:
-            self.calculate_grids_strides()
-
         logger.info(f"ONNX: {path} loaded")
 
     def detect_raw(self, tensor_input: np.ndarray):
@@ -70,12 +66,10 @@ class ONNXDetector(DetectionApi):
                 None,
                 {
                     "images": tensor_input,
-                    "orig_target_sizes": np.array(
-                        [[self.height, self.width]], dtype=np.int64
-                    ),
+                    "orig_target_sizes": np.array([[self.h, self.w]], dtype=np.int64),
                 },
             )
-            return post_process_dfine(tensor_output, self.width, self.height)
+            return post_process_dfine(tensor_output, self.w, self.h)
 
         model_input_name = self.model.get_inputs()[0].name
         tensor_output = self.model.run(None, {model_input_name: tensor_input})
@@ -97,22 +91,14 @@ class ONNXDetector(DetectionApi):
                 detections[i] = [
                     class_id,
                     confidence,
-                    y_min / self.height,
-                    x_min / self.width,
-                    y_max / self.height,
-                    x_max / self.width,
+                    y_min / self.h,
+                    x_min / self.w,
+                    y_max / self.h,
+                    x_max / self.w,
                 ]
             return detections
         elif self.onnx_model_type == ModelTypeEnum.yologeneric:
-            return post_process_yolo(tensor_output, self.width, self.height)
-        elif self.onnx_model_type == ModelTypeEnum.yolox:
-            return post_process_yolox(
-                tensor_output[0],
-                self.width,
-                self.height,
-                self.grids,
-                self.expanded_strides,
-            )
+            return post_process_yolo(tensor_output, self.w, self.h)
         else:
             raise Exception(
                 f"{self.onnx_model_type} is currently not supported for onnx. See the docs for more info on supported models."

frigate/detectors/plugins/openvino.py

@@ -38,7 +38,6 @@ class OvDetector(DetectionApi):
     ]
 
     def __init__(self, detector_config: OvDetectorConfig):
-        super().__init__(detector_config)
         self.ov_core = ov.Core()
         self.ov_model_type = detector_config.model.model_type
 
@@ -134,7 +133,25 @@ class OvDetector(DetectionApi):
                     break
             self.num_classes = tensor_shape[2] - 5
             logger.info(f"YOLOX model has {self.num_classes} classes")
-            self.calculate_grids_strides()
+            self.set_strides_grids()
+
+    def set_strides_grids(self):
+        grids = []
+        expanded_strides = []
+
+        strides = [8, 16, 32]
+
+        hsize_list = [self.h // stride for stride in strides]
+        wsize_list = [self.w // stride for stride in strides]
+
+        for hsize, wsize, stride in zip(hsize_list, wsize_list, strides):
+            xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize))
+            grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+            grids.append(grid)
+            shape = grid.shape[:2]
+            expanded_strides.append(np.full((*shape, 1), stride))
+        self.grids = np.concatenate(grids, 1)
+        self.expanded_strides = np.concatenate(expanded_strides, 1)
 
     ## Takes in class ID, confidence score, and array of [x, y, w, h] that describes detection position,
     ## returns an array that's easily passable back to Frigate.

frigate/detectors/plugins/rknn.py

@@ -4,7 +4,6 @@ import re
 import urllib.request
 from typing import Literal
 
-import numpy as np
 from pydantic import Field
 
 from frigate.const import MODEL_CACHE_DIR
@@ -151,62 +150,6 @@ class Rknn(DetectionApi):
                 'Make sure to set the model input_tensor to "nhwc" in your config.'
             )
 
-    def post_process_yolonas(self, output: list[np.ndarray]):
-        """
-        @param output: output of inference
-        expected shape: [np.array(1, N, 4), np.array(1, N, 80)]
-        where N depends on the input size e.g. N=2100 for 320x320 images
-
-        @return: best results: np.array(20, 6) where each row is
-        in this order (class_id, score, y1/height, x1/width, y2/height, x2/width)
-        """
-
-        N = output[0].shape[1]
-
-        boxes = output[0].reshape(N, 4)
-        scores = output[1].reshape(N, 80)
-
-        class_ids = np.argmax(scores, axis=1)
-        scores = scores[np.arange(N), class_ids]
-
-        args_best = np.argwhere(scores > self.thresh)[:, 0]
-
-        num_matches = len(args_best)
-        if num_matches == 0:
-            return np.zeros((20, 6), np.float32)
-        elif num_matches > 20:
-            args_best20 = np.argpartition(scores[args_best], -20)[-20:]
-            args_best = args_best[args_best20]
-
-        boxes = boxes[args_best]
-        class_ids = class_ids[args_best]
-        scores = scores[args_best]
-
-        boxes = np.transpose(
-            np.vstack(
-                (
-                    boxes[:, 1] / self.height,
-                    boxes[:, 0] / self.width,
-                    boxes[:, 3] / self.height,
-                    boxes[:, 2] / self.width,
-                )
-            )
-        )
-
-        results = np.hstack(
-            (class_ids[..., np.newaxis], scores[..., np.newaxis], boxes)
-        )
-
-        return np.resize(results, (20, 6))
-
-    def post_process(self, output):
-        if self.detector_config.model.model_type == ModelTypeEnum.yolonas:
-            return self.post_process_yolonas(output)
-        else:
-            raise ValueError(
-                f'Model type "{self.detector_config.model.model_type}" is currently not supported.'
-            )
-
     def detect_raw(self, tensor_input):
         output = self.rknn.inference(
             [

frigate/object_detection/base.py

@@ -77,8 +77,6 @@ class LocalObjectDetector(ObjectDetector):
         if self.dtype == InputDTypeEnum.float:
             tensor_input = tensor_input.astype(np.float32)
             tensor_input /= 255
-        elif self.dtype == InputDTypeEnum.float_denorm:
-            tensor_input = tensor_input.astype(np.float32)
 
         return self.detect_api.detect_raw(tensor_input=tensor_input)
 

frigate/track/tracked_object.py

@@ -138,13 +138,11 @@ class TrackedObject:
 
         if not self.false_positive and has_valid_frame:
             # determine if this frame is a better thumbnail
-            if self.thumbnail_data is None or (
-                better_thumb := is_better_thumbnail(
-                    self.obj_data["label"],
-                    self.thumbnail_data,
-                    obj_data,
-                    self.camera_config.frame_shape,
-                )
+            if self.thumbnail_data is None or is_better_thumbnail(
+                self.obj_data["label"],
+                self.thumbnail_data,
+                obj_data,
+                self.camera_config.frame_shape,
             ):
                 # use the current frame time if the object's frame time isn't in the frame cache
                 selected_frame_time = (
@@ -152,13 +150,6 @@ class TrackedObject:
                     if obj_data["frame_time"] not in self.frame_cache.keys()
                     else obj_data["frame_time"]
                 )
-                if (
-                    obj_data["frame_time"] not in self.frame_cache.keys()
-                    and not better_thumb
-                ):
-                    logger.warning(
-                        f"Frame time {obj_data['frame_time']} not in frame cache, using current frame time {selected_frame_time}"
-                    )
                 self.thumbnail_data = {
                     "frame_time": selected_frame_time,
                     "box": obj_data["box"],

frigate/util/model.py

@@ -148,17 +148,27 @@ def __post_process_multipart_yolo(
                     bw = ((dw * 2.0) ** 2) * anchor_w
                     bh = ((dh * 2.0) ** 2) * anchor_h
 
-                    x1 = max(0, bx - bw / 2)
-                    y1 = max(0, by - bh / 2)
-                    x2 = min(width, bx + bw / 2)
-                    y2 = min(height, by + bh / 2)
+                    x1 = max(0, bx - bw / 2) / width
+                    y1 = max(0, by - bh / 2) / height
+                    x2 = min(width, bx + bw / 2) / width
+                    y2 = min(height, by + bh / 2) / height
 
                     all_boxes.append([x1, y1, x2, y2])
                     all_scores.append(conf)
                     all_class_ids.append(class_id)
 
+    formatted_boxes = [
+        [
+            int(x1 * width),
+            int(y1 * height),
+            int((x2 - x1) * width),
+            int((y2 - y1) * height),
+        ]
+        for x1, y1, x2, y2 in all_boxes
+    ]
+
     indices = cv2.dnn.NMSBoxes(
-        bboxes=all_boxes,
+        bboxes=formatted_boxes,
         scores=all_scores,
         score_threshold=0.4,
         nms_threshold=0.4,
@@ -171,25 +181,13 @@ def __post_process_multipart_yolo(
             class_id = all_class_ids[idx]
             conf = all_scores[idx]
             x1, y1, x2, y2 = all_boxes[idx]
-            results[i] = [
-                class_id,
-                conf,
-                y1 / height,
-                x1 / width,
-                y2 / height,
-                x2 / width,
-            ]
+            results[i] = [class_id, conf, y1, x1, y2, x2]
 
     return np.array(results, dtype=np.float32)
 
 
 def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarray:
-    predictions = np.squeeze(predictions)
-
-    # transpose the output so it has order (inferences, class_ids)
-    if predictions.shape[0] < predictions.shape[1]:
-        predictions = predictions.T
-
+    predictions = np.squeeze(predictions).T
     scores = np.max(predictions[:, 4:], axis=1)
     predictions = predictions[scores > 0.4, :]
     scores = scores[scores > 0.4]
@@ -197,14 +195,9 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
 
     # Rescale box
     boxes = predictions[:, :4]
-    boxes_xyxy = np.ones_like(boxes)
-    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
-    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
-    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2
-    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2
-    boxes = boxes_xyxy
 
-    # run NMS
+    input_shape = np.array([width, height, width, height])
+    boxes = np.divide(boxes, input_shape, dtype=np.float32)
     indices = cv2.dnn.NMSBoxes(boxes, scores, score_threshold=0.4, nms_threshold=0.4)
     detections = np.zeros((20, 6), np.float32)
     for i, (bbox, confidence, class_id) in enumerate(
@@ -216,10 +209,10 @@ def __post_process_nms_yolo(predictions: np.ndarray, width, height) -> np.ndarra
         detections[i] = [
             class_id,
             confidence,
-            bbox[1] / height,
-            bbox[0] / width,
-            bbox[3] / height,
-            bbox[2] / width,
+            bbox[1] - bbox[3] / 2,
+            bbox[0] - bbox[2] / 2,
+            bbox[1] + bbox[3] / 2,
+            bbox[0] + bbox[2] / 2,
         ]
 
     return detections
@@ -232,53 +225,6 @@ def post_process_yolo(output: list[np.ndarray], width: int, height: int) -> np.n
         return __post_process_nms_yolo(output[0], width, height)
 
 
-def post_process_yolox(
-    predictions: np.ndarray,
-    width: int,
-    height: int,
-    grids: np.ndarray,
-    expanded_strides: np.ndarray,
-) -> np.ndarray:
-    predictions[..., :2] = (predictions[..., :2] + grids) * expanded_strides
-    predictions[..., 2:4] = np.exp(predictions[..., 2:4]) * expanded_strides
-
-    # process organized predictions
-    predictions = predictions[0]
-    boxes = predictions[:, :4]
-    scores = predictions[:, 4:5] * predictions[:, 5:]
-
-    boxes_xyxy = np.ones_like(boxes)
-    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2
-    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2
-    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2
-    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2
-
-    cls_inds = scores.argmax(1)
-    scores = scores[np.arange(len(cls_inds)), cls_inds]
-
-    indices = cv2.dnn.NMSBoxes(
-        boxes_xyxy, scores, score_threshold=0.4, nms_threshold=0.4
-    )
-
-    detections = np.zeros((20, 6), np.float32)
-    for i, (bbox, confidence, class_id) in enumerate(
-        zip(boxes_xyxy[indices], scores[indices], cls_inds[indices])
-    ):
-        if i == 20:
-            break
-
-        detections[i] = [
-            class_id,
-            confidence,
-            bbox[1] / height,
-            bbox[0] / width,
-            bbox[3] / height,
-            bbox[2] / width,
-        ]
-
-    return detections
-
-
 ### ONNX Utilities