diff --git a/comfy_extras/nodes_mediapipe.py b/comfy_extras/nodes_mediapipe.py
index 2e67ae83f..6b7916aee 100644
--- a/comfy_extras/nodes_mediapipe.py
+++ b/comfy_extras/nodes_mediapipe.py
@@ -28,7 +28,7 @@ from comfy_extras.mediapipe.face_landmarker import FaceLandmarker
 from comfy_extras.mediapipe.face_geometry import transformation_matrix_from_detection
 
 
-FaceLandmarkerType = io.Custom("FACE_LANDMARKER")
+FaceDetectionType = io.Custom("FACE_DETECTION_MODEL")
 FaceLandmarksType = io.Custom("FACE_LANDMARKS")
 
 _CANONICAL_KEYS = ("canonical_vertices", "procrustes_indices", "procrustes_weights")
@@ -204,18 +204,19 @@ class LoadMediaPipeFaceLandmarker(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="LoadMediaPipeFaceLandmarker",
-            display_name="Load MediaPipe Face Landmarker",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
+            display_name="Load Face Detection Model (MediaPipe)",
             category="loaders",
             inputs=[
-                io.Combo.Input("model_name", options=folder_paths.get_filename_list("mediapipe"),
-                               tooltip="Face Landmarker safetensors from models/mediapipe/."),
+                io.Combo.Input("model_name", options=folder_paths.get_filename_list("detection"),
+                               tooltip="Face detection model from models/detection/."),
             ],
-            outputs=[FaceLandmarkerType.Output()],
+            outputs=[FaceDetectionType.Output()],
         )
 
     @classmethod
     def execute(cls, model_name) -> io.NodeOutput:
-        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("mediapipe", model_name), safe_load=True)
+        sd = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("detection", model_name), safe_load=True)
         wrapper = FaceLandmarkerModel(sd)
         return io.NodeOutput(wrapper)
 
@@ -234,10 +235,12 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceLandmarker",
-            display_name="MediaPipe Face Landmarker",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection"],
+            display_name="Detect Face Landmarks (MediaPipe)",
             category="image/detection",
+            description="Detects facial landmarks using MediaPipe model.",
             inputs=[
-                FaceLandmarkerType.Input("face_landmarker"),
+                FaceDetectionType.Input("face_detection_model"),
                 io.Image.Input("image"),
                 io.Combo.Input("detector_variant", options=["short", "full", "both"], default="short",
                                tooltip="Face detector range. 'short' is tuned for close-up faces "
@@ -261,9 +264,9 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
         )
 
     @classmethod
-    def execute(cls, face_landmarker, image, detector_variant, num_faces, min_confidence,
+    def execute(cls, face_detection_model, image, detector_variant, num_faces, min_confidence,
                 missing_frame_fallback) -> io.NodeOutput:
-        canonical = face_landmarker.canonical_data
+        canonical = face_detection_model.canonical_data
         img_np = _image_to_uint8(image)
         B, H, W = img_np.shape[:3]
         chunk = 16
@@ -276,7 +279,7 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
             with tqdm(total=B, desc=f"MediaPipe Face Landmarker ({variant})") as tq:
                 for i in range(0, B, chunk):
                     end = min(i + chunk, B)
-                    res.extend(face_landmarker.detect_batch(
+                    res.extend(face_detection_model.detect_batch(
                         [img_np[bi] for bi in range(i, end)],
                         num_faces=int(num_faces),
                         score_thresh=float(min_confidence),
@@ -306,7 +309,7 @@ class MediaPipeFaceLandmarker(io.ComfyNode):
                 per_bb.append({"x": x1, "y": y1, "width": x2 - x1, "height": y2 - y1, "label": "face", "score": float(f["score"])})
             bboxes.append(per_bb)
         return io.NodeOutput({"frames": frames, "image_size": (H, W),
-                              "connection_sets": face_landmarker.connection_sets}, bboxes)
+                              "connection_sets": face_detection_model.connection_sets}, bboxes)
 
 
 # Topology keys unioned by the 'all' connections preset (contour parts + irises + nose).
@@ -332,8 +335,10 @@ class MediaPipeFaceMeshVisualize(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceMeshVisualize",
-            display_name="MediaPipe Face Mesh Visualize",
+            search_aliases=["face", "facial", "mediapipe", "face landmark", "face mesh", "blazeface", "face detection", "visualize"],
+            display_name="Visualize Face Landmarks (MediaPipe)",
             category="image/detection",
+            description="Draws face landmarks mesh on the input image.",
             inputs=[
                 FaceLandmarksType.Input("face_landmarks"),
                 io.Image.Input("image", optional=True, tooltip="If not connected, a black canvas will be used."),
@@ -443,8 +448,10 @@ class MediaPipeFaceMask(io.ComfyNode):
     def define_schema(cls):
         return io.Schema(
             node_id="MediaPipeFaceMask",
-            display_name="MediaPipe Face Mask",
+            search_aliases=["face", "facial", "mediapipe", "face mask", "blazeface", "face detection", "visualize"],
+            display_name="Draw Face Mask (MediaPipe)",
             category="image/detection",
+            description="Draws a mask from face landmarks.",
             inputs=[
                 FaceLandmarksType.Input("face_landmarks"),
                 io.DynamicCombo.Input(
diff --git a/folder_paths.py b/folder_paths.py
index ce152eb37..36d61fcd0 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -60,7 +60,7 @@ folder_names_and_paths["geometry_estimation"] = ([os.path.join(models_dir, "geom
 
 folder_names_and_paths["optical_flow"] = ([os.path.join(models_dir, "optical_flow")], supported_pt_extensions)
 
-folder_names_and_paths["mediapipe"] = ([os.path.join(models_dir, "mediapipe")], supported_pt_extensions)
+folder_names_and_paths["detection"] = ([os.path.join(models_dir, "detection")], supported_pt_extensions)
 
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
diff --git a/models/mediapipe/put_mediapipe_models_here b/models/detection/put_detection_models_here
similarity index 100%
rename from models/mediapipe/put_mediapipe_models_here
rename to models/detection/put_detection_models_here