diff --git a/docker/main/requirements-wheels.txt b/docker/main/requirements-wheels.txt
index 885d5e244..4a1e0fc24 100644
--- a/docker/main/requirements-wheels.txt
+++ b/docker/main/requirements-wheels.txt
@@ -80,4 +80,5 @@ faster-whisper==1.1.*
 librosa==0.11.*
 soundfile==0.13.*
 # DeGirum detector
-degirum_headless == 0.15.*
+degirum == 0.16.*
+# degirum_headless == 0.15.*
diff --git a/frigate/detectors/plugins/degirum.py b/frigate/detectors/plugins/degirum.py
index fa643c510..1b414507a 100644
--- a/frigate/detectors/plugins/degirum.py
+++ b/frigate/detectors/plugins/degirum.py
@@ -87,43 +87,135 @@ class DGDetector(DetectionApi):
         self._zoo = dg.connect(
             detector_config.location, detector_config.zoo, detector_config.token
         )
+        logger.info(f"Models in zoo: {self._zoo.list_models()}")
         self.dg_model = self._zoo.load_model(
-            detector_config.model.path, non_blocking_batch_predict=True
+            detector_config.model.path,
         )
+        self.dg_model.measure_time = True
+        self.dg_model.input_image_format = "RAW"
+        self.dg_model._postprocessor = None
         # Openvino tends to have multidevice, and they default to CPU rather than GPU or NPU
         types = self.dg_model.supported_device_types
         for type in types:
             # If openvino is supported, prioritize using gpu, then npu, then cpu
             if "OPENVINO" in type:
                 self.dg_model.device_type = [
-                    "OPENVINO/GPU",
-                    "OPENVINO/NPU",
+                    # "OPENVINO/GPU",
+                    # "OPENVINO/NPU",
                     "OPENVINO/CPU",
                 ]
+            elif "HAILORT" in type:
+                self.dg_model.device_type = [
+                    "HAILORT/HAILO8l",
+                    "HAILORT/HAILO8",
+                ]
             break
-        self.model_height = detector_config.model.height
-        self.model_width = detector_config.model.width
-        self.predict_batch = self.dg_model.predict_batch(self._queue)
+        input_shape = self.dg_model.input_shape[0]
+        self.model_height = input_shape[1]
+        self.model_width = input_shape[2]
+
+        frame = np.zeros(
+            (detector_config.model.width, detector_config.model.height, 3),
+            dtype=np.uint8,
+        )
+        self.dg_model(frame)
+        self.prediction = self.prediction_generator()
+        self.none_counter = 0
+        self.not_none_counter = 0
+        self.overall_frame_counter = 0
+        self.times = 0
+
+    def prediction_generator(self):
+        # logger.debug("Prediction generator was called")
+        with self.dg_model as model:
+            while 1:
+                # logger.debug(f"q size before calling get: {self._queue.qsize()}")
+                data = self._queue.get()
+                # logger.debug(f"q size after calling get: {self._queue.qsize()}")
+                # logger.debug(
+                #     f"Data we're passing into model predict: {data}, shape of data: {data.shape}"
+                # )
+                start = time.time_ns()
+                result = model.predict(data)
+                self.times += (time.time_ns() - start) * 1e-6
+                # logger.info(
+                #     f"Entire time taken to get result back: {self.times / self.overall_frame_counter}"
+                # )
+                yield result
 
     def detect_raw(self, tensor_input):
-        # add tensor_input to input queue
+        # start = time.time_ns()
+        self.overall_frame_counter += 1
         truncated_input = tensor_input.reshape(tensor_input.shape[1:])
-        self._queue.put((truncated_input, ""))
+        # logger.debug(f"Detect raw was called for tensor input: {tensor_input}")
+
+        # add tensor_input to input queue
+        self._queue.put(truncated_input)
+        # logger.debug(f"Queue size after adding truncated input: {self._queue.qsize()}")
 
         # define empty detection result
         detections = np.zeros((20, 6), np.float32)
-        res = next(self.predict_batch)
-        if res is not None:
+        # res = next(self.prediction)
+        result = next(self.prediction)
+        # return detections
+        # result = self.prediction_generator()
+        # logger.info(f"Result: {result}")
+        # logger.info(f"Shape of res: {res.results[0]["data"]}")
+        # logger.debug(f"Queue size after calling for res: {self._queue.qsize()}")
+        # logger.debug(f"Output of res in initial next call: {res}")
+        # logger.info(
+        # f"Overall frame number: {self.overall_frame_counter}, none count: {self.none_counter}, not none count: {self.not_none_counter}, none percentage: {self.none_counter / self.overall_frame_counter}"
+        # )
+        # logger.info(f"Time stats right after res: {self.dg_model.time_stats()}")
+        # start = time.time_ns()
+
+        # res_string = str(res)
+        # logger.info(f"Res is: {res_string}")
+        # logger.debug(f"Res's list of attributes: {dir(res)}")
+        # logger.debug(
+        #     f"Res results, {res.results}, length of results: {len(res.results)}"
+        # )
+        # logger.info(f"Output of res: {res}")
+        # res_string = str(res)
+        # logger.info(f"Data from array: {res.results}")
+        # logger.info(f"First data: {res.results[0]['data']}")
+        # logger.info(f"Length of data: {len(res.results[0]['data'][0])}")
+        # if res is not None and res.results[0].get("category_id") is not None:
+        if result is not None:
             # populate detection result with corresponding inference result information
+            # self.not_none_counter += 1
             i = 0
-            for result in res.results:
-                detections[i] = [
-                    result["category_id"],  # Label ID
-                    float(result["score"]),  # Confidence
-                    result["bbox"][1] / self.model_height,  # y_min
-                    result["bbox"][0] / self.model_width,  # x_min
-                    result["bbox"][3] / self.model_height,  # y_max
-                    result["bbox"][2] / self.model_width,  # x_max
-                ]
+
+            # for result in res.results:
+            #     if i > 20:
+            #         break
+
+            #     detections[i] = [
+            #         result["category_id"],
+            #         float(result["score"]),
+            #         result["bbox"][1] / self.model_height,
+            #         result["bbox"][0] / self.model_width,
+            #         result["bbox"][3] / self.model_height,
+            #         result["bbox"][2] / self.model_width,
+            #     ]
+            #     i += 1
+
+            for item in result.results:
+                # logger.info(f"CURRENT ITEM: {item}")
+                if i >= 20:
+                    break
+
+                category_id = int(item[5])
+                score = item[4]
+                y_min = item[1]
+                x_min = item[0]
+                x_max = item[2]
+                y_max = item[3]
+                detections[i] = [category_id, score, y_min, x_min, y_max, x_max]
                 i += 1
+
+        if detections[0][1] != 0:  # if we have a score, then print detection
+            logger.info(f"Output of detections: {detections}")
+        ## Save the detection results to a file so we can compare
+        # logger.info(f"Overall time took: {(time.time_ns() - start) * 1e-6}ms")
         return detections
diff --git a/model/ssdlite_mobilenet_v2--openvino1/labels.json b/model/ssdlite_mobilenet_v2--openvino1/labels.json
new file mode 100644
index 000000000..67577c47b
--- /dev/null
+++ b/model/ssdlite_mobilenet_v2--openvino1/labels.json
@@ -0,0 +1,95 @@
+{
+  "0": "__background__",
+  "1": "person",
+  "2": "bicycle",
+  "3": "car",
+  "4": "motorcycle",
+  "5": "airplan",
+  "6": "bus",
+  "7": "train",
+  "8": "car",
+  "9": "boat",
+  "10": "traffic light",
+  "11": "fire hydrant",
+  "12": "street sign",
+  "13": "stop sign",
+  "14": "parking meter",
+  "15": "bench",
+  "16": "bird",
+  "17": "cat",
+  "18": "dog",
+  "19": "horse",
+  "20": "sheep",
+  "21": "cow",
+  "22": "elephant",
+  "23": "bear",
+  "24": "zebra",
+  "25": "giraffe",
+  "26": "hat",
+  "27": "backpack",
+  "28": "umbrella",
+  "29": "shoe",
+  "30": "eye glasses",
+  "31": "handbag",
+  "32": "tie",
+  "33": "suitcase",
+  "34": "frisbee",
+  "35": "skis",
+  "36": "snowboard",
+  "37": "sports ball",
+  "38": "kite",
+  "39": "baseball bat",
+  "40": "baseball glove",
+  "41": "skateboard",
+  "42": "surfboard",
+  "43": "tennis racket",
+  "44": "bottle",
+  "45": "plate",
+  "46": "wine glass",
+  "47": "cup",
+  "48": "fork",
+  "49": "knife",
+  "50": "spoon",
+  "51": "bowl",
+  "52": "banana",
+  "53": "apple",
+  "54": "sandwich",
+  "55": "orange",
+  "56": "broccoli",
+  "57": "carrot",
+  "58": "hot dog",
+  "59": "pizza",
+  "60": "donut",
+  "61": "cake",
+  "62": "chair",
+  "63": "couch",
+  "64": "potted plant",
+  "65": "bed",
+  "66": "mirror",
+  "67": "dining table",
+  "68": "window",
+  "69": "desk",
+  "70": "toilet",
+  "71": "door",
+  "72": "tv",
+  "73": "laptop",
+  "74": "mouse",
+  "75": "remote",
+  "76": "keyboard",
+  "77": "cell phone",
+  "78": "microwave",
+  "79": "oven",
+  "80": "toaster",
+  "81": "sink",
+  "82": "refrigerator",
+  "83": "blender",
+  "84": "book",
+  "85": "clock",
+  "86": "vase",
+  "87": "scissors",
+  "88": "teddy bear",
+  "89": "hair drier",
+  "90": "toothbrush",
+  "91": "hair brush"
+}
+
diff --git a/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.bin b/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.bin
new file mode 100644
index 000000000..339eed5bb
Binary files /dev/null and b/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.bin differ
diff --git a/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.json b/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.json
new file mode 100644
index 000000000..e9d06fb06
--- /dev/null
+++ b/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.json
@@ -0,0 +1,53 @@
+{
+  "ConfigVersion": 6,
+  "Checksum": "0ebce8b115214756bd37cfb5b4c3b547d557c6c58e828a8b9f725214afe49600",
+  "DEVICE": [
+    {
+      "RuntimeAgent": "OPENVINO",
+      "DeviceType": "CPU",
+      "SupportedDeviceTypes": "OPENVINO/CPU"
+    }
+  ],
+  "MODEL_PARAMETERS": [
+    {
+      "ModelPath": "ssdlite_mobilenet_v2.xml"
+    }
+  ],
+  "PRE_PROCESS": [
+    {
+      "InputImgFmt": "JPEG",
+      "InputImgNormEn": false,
+      "InputN": 1,
+      "InputType": "Image",
+      "InputResizeMethod": "bilinear",
+      "InputPadMethod": "letterbox",
+      "ImageBackend": "auto",
+      "InputH": 300,
+      "InputW": 300,
+      "InputC": 3,
+      "InputQuantEn": true,
+      "InputQuantOffset": 0,
+      "InputQuantScale": 1,
+      "InputTensorLayout": "NCHW",
+      "InputImgSliceType": "None"
+    }
+  ],
+  "POST_PROCESS": [
+    {
+      "PostProcessorInputs": [3, 1, 2],
+      "OutputPostprocessType": "Detection",
+      "LabelsPath": "labels.json",
+      "OutputConfThreshold": 0.3,
+      "MaxDetections": 20,
+      "OutputNMSThreshold": 0.6,
+      "MaxDetectionsPerClass": 100,
+      "MaxClassesPerDetection": 1,
+      "UseRegularNMS": false,
+      "OutputNumClasses": 90,
+      "XScale": 10,
+      "YScale": 10,
+      "HScale": 5,
+      "WScale": 5
+    }
+  ]
+}
diff --git a/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.xml b/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.xml
new file mode 100644
index 000000000..105cb0567
--- /dev/null
+++ b/model/ssdlite_mobilenet_v2--openvino1/ssdlite_mobilenet_v2--openvino1.xml
@@ -0,0 +1,14389 @@
+<?xml version="1.0"?>
+<net name="frozen_inference_graph" version="11">
+	<layers>
+		<layer id="0" name="image_tensor" type="Parameter" version="opset1">
+			<data shape="1,300,300,3" element_type="u8" />
+			<rt_info>
+				<attribute name="old_api_map_element_type" version="0" value="f32" />
+				<attribute name="old_api_map_order" version="0" value="0, 2, 3, 1" />
+			</rt_info>
+			<output>
+				<port id="0" precision="U8" names="image_tensor:0">
+					<dim>1</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="Preprocessor/mul/x_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="1, 1, 1, 1" offset="0" size="2" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Preprocessor/mul/x" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="Preprocessor/mul/x:0">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="Convert_2822" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<input>
+				<port id="0" precision="U8">
+					<dim>1</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="image_tensor/Transpose([0 3 1 2])/value2429333834" type="Const" version="opset1">
+			<data element_type="i64" shape="4" offset="2" size="32" />
+			<output>
+				<port id="0" precision="I64">
+					<dim>4</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="ToFloat" type="Transpose" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+					<dim>3</dim>
+				</port>
+				<port id="1" precision="I64">
+					<dim>4</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="Preprocessor/mul" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="Preprocessor/mul:0">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="Preprocessor/sub/y_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="1, 1, 1, 1" offset="34" size="2" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="Preprocessor/sub/y" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="Preprocessor/sub/y:0">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="Preprocessor/sub" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="FeatureExtractor/MobilenetV2/MobilenetV2/input:0,Preprocessor/sub:0">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="Gather_3200_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="32, 3, 3, 3" offset="36" size="1728" />
+			<rt_info>
+				<attribute name="preprocessing" version="0" />
+			</rt_info>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="Gather_3200" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+				<attribute name="preprocessing" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="FeatureExtractor/MobilenetV2/Conv/BatchNorm/batchnorm/mul_1" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="same_upper" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>300</dim>
+					<dim>300</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="FeatureExtractor/MobilenetV2/Conv/BatchNorm/batchnorm/mul_1:0">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>150</dim>
+					<dim>150</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="FeatureExtractor/MobilenetV2/Conv/BatchNorm/batchnorm/sub/_100__cf__103_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="1, 32, 1, 1" offset="1764" size="64" />
+			<output>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
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+			<input_model value="DIR/frozen_inference_graph.pb" />
+			<is_python_api_used value="True" />
+			<reverse_input_channels value="True" />
+			<tensorflow_object_detection_api_pipeline_config value="DIR/pipeline.config" />
+			<transformations_config value="DIR/ssd_v2_support.json" />
+		</conversion_parameters>
+		<legacy_frontend value="True" />
+	</rt_info>
+</net>