diff --git a/frigate/db/sqlitevecq.py b/frigate/db/sqlitevecq.py
index 1447fd48f..d630e1ddf 100644
--- a/frigate/db/sqlitevecq.py
+++ b/frigate/db/sqlitevecq.py
@@ -63,6 +63,6 @@ class SqliteVecQueueDatabase(SqliteQueueDatabase):
             self.execute_sql("""
                 CREATE VIRTUAL TABLE IF NOT EXISTS vec_faces USING vec0(
                     id TEXT PRIMARY KEY,
-                    face_embedding FLOAT[128] distance_metric=cosine
+                    face_embedding FLOAT[512] distance_metric=cosine
                 );
             """)
diff --git a/frigate/embeddings/embeddings.py b/frigate/embeddings/embeddings.py
index a2de88394..23b8aa7ee 100644
--- a/frigate/embeddings/embeddings.py
+++ b/frigate/embeddings/embeddings.py
@@ -133,7 +133,7 @@ class Embeddings:
                 model_name="facenet",
                 model_file="facenet.onnx",
                 download_urls={
-                    "facenet.onnx": "https://github.com/NicolasSM-001/faceNet.onnx-/raw/refs/heads/main/faceNet.onnx",
+                    "facenet.onnx": "https://github.com/NickM-27/facenet-onnx/releases/download/v1.0/facenet.onnx",
                     "facedet.onnx": "https://github.com/opencv/opencv_zoo/raw/refs/heads/main/models/face_detection_yunet/face_detection_yunet_2023mar_int8.onnx",
                 },
                 model_size="large",
diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index 200f728d3..035dc1cc2 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/frigate/embeddings/functions/onnx.py
@@ -222,7 +222,7 @@ class GenericONNXEmbedding:
             frame[y_center : y_center + og_h, x_center : x_center + og_w] = og
 
             frame = np.expand_dims(frame, axis=0)
-            return [{"image_input": frame}]
+            return [{"input_2": frame}]
         elif self.model_type == ModelTypeEnum.alpr_detect:
             preprocessed = []
             for x in raw_inputs: