diff --git a/frigate/detectors/detector_config.py b/frigate/detectors/detector_config.py
index d87405d08..5370ec518 100644
--- a/frigate/detectors/detector_config.py
+++ b/frigate/detectors/detector_config.py
@@ -31,6 +31,7 @@ class InputTensorEnum(str, Enum):
 
 class InputDTypeEnum(str, Enum):
     float = "float"
+    float_denorm = "float_denorm"  # non-normalized float
     int = "int"
 
 
@@ -208,4 +209,4 @@ class BaseDetectorConfig(BaseModel):
     )
     model_config = ConfigDict(
         extra="allow", arbitrary_types_allowed=True, protected_namespaces=()
-    )
+    )
\ No newline at end of file
diff --git a/frigate/detectors/plugins/memryx.py b/frigate/detectors/plugins/memryx.py
index 3c710ec86..f2aec91c0 100644
--- a/frigate/detectors/plugins/memryx.py
+++ b/frigate/detectors/plugins/memryx.py
@@ -212,9 +212,8 @@ class MemryXDetector(DetectionApi):
             raise ValueError("[send_input] No image data provided for inference")
 
         if self.memx_model_type == ModelTypeEnum.yolox:
-            tensor_input = tensor_input.squeeze(0)
+            tensor_input = tensor_input.squeeze(2)
 
-            tensor_input = tensor_input * 255.0
             padded_img = np.ones((640, 640, 3), dtype=np.uint8) * 114
 
             scale = min(
@@ -238,15 +237,10 @@ class MemryXDetector(DetectionApi):
 
             # Step 5: Concatenate along the channel dimension (axis 2)
             concatenated_img = np.concatenate([x0, x1, x2, x3], axis=2)
-            processed_input = concatenated_img.astype(np.float32)
-            
-        else: 
-            tensor_input = tensor_input.squeeze(0)  # (H, W, C)
-            # Add axis=2 to create Z=1: (H, W, Z=1, C)
-            processed_input = np.expand_dims(tensor_input, axis=2)  # Now (H, W, 1, 3)
+            tensor_input = concatenated_img.astype(np.float32)
 
         # Send frame to MemryX for processing
-        self.capture_queue.put(processed_input)
+        self.capture_queue.put(tensor_input)
         self.capture_id_queue.put(connection_id)
 
     def process_input(self):