diff --git a/deepface/DeepFace.py b/deepface/DeepFace.py
index 51cc8e2..9f3ff1e 100644
--- a/deepface/DeepFace.py
+++ b/deepface/DeepFace.py
@@ -2,7 +2,7 @@
 import os
 import warnings
 import logging
-from typing import Any, Dict, IO, List, Union, Optional
+from typing import Any, Dict, IO, List, Union, Optional, Sequence
 
 # this has to be set before importing tensorflow
 os.environ["TF_USE_LEGACY_KERAS"] = "1"
@@ -373,7 +373,7 @@ def find(
 
 
 def represent(
-    img_path: Union[str, np.ndarray, IO[bytes], List[Union[str, np.ndarray]]],
+    img_path: Union[str, np.ndarray, IO[bytes], Sequence[Union[str, np.ndarray, IO[bytes]]]],
     model_name: str = "VGG-Face",
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
@@ -387,10 +387,10 @@ def represent(
     Represent facial images as multi-dimensional vector embeddings.
 
     Args:
-        img_path (str, np.ndarray, IO[bytes], or List[Union[str, np.ndarray]]): The exact path to the image, a numpy array
+        img_path (str, np.ndarray, IO[bytes], or Sequence[Union[str, np.ndarray, IO[bytes]]]): The exact path to the image, a numpy array
             in BGR format, a file object that supports at least `.read` and is opened in binary
             mode, or a base64 encoded image. If the source image contains multiple faces,
-            the result will include information for each detected face. If a list is provided,
+            the result will include information for each detected face. If a sequence is provided,
             each element should be a string or numpy array representing an image, and the function
             will process images in batch.
 
diff --git a/deepface/modules/representation.py b/deepface/modules/representation.py
index 6bdbf1b..d36f5bc 100644
--- a/deepface/modules/representation.py
+++ b/deepface/modules/representation.py
@@ -1,5 +1,5 @@
 # built-in dependencies
-from typing import Any, Dict, List, Union, Optional
+from typing import Any, Dict, List, Union, Optional, Sequence, IO
 
 # 3rd party dependencies
 import numpy as np
@@ -11,7 +11,7 @@ from deepface.models.FacialRecognition import FacialRecognition
 
 
 def represent(
-    img_path: Union[str, np.ndarray, List[Union[str, np.ndarray]]],
+    img_path: Union[str, IO[bytes], np.ndarray, Sequence[Union[str, np.ndarray, IO[bytes]]]],
     model_name: str = "VGG-Face",
     enforce_detection: bool = True,
     detector_backend: str = "opencv",
@@ -25,8 +25,8 @@ def represent(
     Represent facial images as multi-dimensional vector embeddings.
 
     Args:
-        img_path (str, np.ndarray, or list): The exact path to the image, a numpy array in BGR format,
-            a base64 encoded image, or a list of these. If the source image contains multiple faces,
+        img_path (str, np.ndarray, or Sequence[Union[str, np.ndarray]]): The exact path to the image, a numpy array in BGR format,
+            a base64 encoded image, or a sequence of these. If the source image contains multiple faces,
             the result will include information for each detected face.
 
         model_name (str): Model for face recognition. Options: VGG-Face, Facenet, Facenet512,