diff --git a/frigate/embeddings/onnx/runner.py b/frigate/embeddings/onnx/runner.py
index 34db4c7fc..6eea44c86 100644
--- a/frigate/embeddings/onnx/runner.py
+++ b/frigate/embeddings/onnx/runner.py
@@ -159,12 +159,10 @@ class RKNNModelRunner:
 
             self.rknn = RKNNLite(verbose=False)
 
-            # Load the RKNN model
             if self.rknn.load_rknn(self.model_path) != 0:
                 logger.error(f"Failed to load RKNN model: {self.model_path}")
                 raise RuntimeError("Failed to load RKNN model")
 
-            # Initialize runtime
             if self.rknn.init_runtime() != 0:
                 logger.error("Failed to initialize RKNN runtime")
                 raise RuntimeError("Failed to initialize RKNN runtime")
@@ -180,23 +178,18 @@ class RKNNModelRunner:
 
     def get_input_names(self) -> list[str]:
         """Get input names for the model."""
-        # RKNN models typically have standard input names
-        # For CLIP models, these are usually "input_ids" and "pixel_values"
+
         if self.model_type and "jina-clip" in self.model_type:
             if "text" in self.model_type:
                 return ["input_ids"]
             elif "vision" in self.model_type:
                 return ["pixel_values"]
 
-        # Default fallback
         return ["input"]
 
     def get_input_width(self) -> int:
         """Get the input width of the model."""
-        # For CLIP vision models, this is typically 224 or 512
-        if self.model_type and "jina-clip-v1-vision" in self.model_type:
-            return 224  # CLIP V1 uses 224x224
-        return -1
+        return 224  # CLIP V1 uses 224x224
 
     def run(self, inputs: dict[str, Any]) -> Any:
         """Run inference with the RKNN model."""
@@ -204,7 +197,6 @@ class RKNNModelRunner:
             raise RuntimeError("RKNN model not loaded")
 
         try:
-            # Convert inputs to the format expected by RKNN
             rknn_inputs = []
             input_names = self.get_input_names()
 
@@ -213,7 +205,6 @@ class RKNNModelRunner:
                     rknn_inputs.append(inputs[name])
                 else:
                     logger.warning(f"Input '{name}' not found in inputs")
-                    # Create a dummy input with appropriate shape
                     if name == "input_ids":
                         rknn_inputs.append(inputs.get("input_ids", [[0]]))
                     elif name == "pixel_values":
@@ -221,7 +212,6 @@ class RKNNModelRunner:
                     else:
                         rknn_inputs.append([[0]])
 
-            # Run inference
             outputs = self.rknn.inference(inputs=rknn_inputs)
             return outputs