diff --git a/detectron2/engine/defaults.py b/detectron2/engine/defaults.py
index c649bf8ff7..0b699666c2 100644
--- a/detectron2/engine/defaults.py
+++ b/detectron2/engine/defaults.py
@@ -16,6 +16,7 @@
 import weakref
 from collections import OrderedDict
 from typing import Optional
+import numpy as np
 import torch
 from fvcore.nn.precise_bn import get_bn_modules
 from omegaconf import OmegaConf
@@ -294,30 +295,49 @@ def __init__(self, cfg):
         self.input_format = cfg.INPUT.FORMAT
         assert self.input_format in ["RGB", "BGR"], self.input_format
 
-    def __call__(self, original_image):
+    def __call__(self, original_images):
         """
         Args:
-            original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
-
+            original_images (np.ndarray or List[np.ndarray]):
+                an image of shape (H, W, C) or (B, H, W, C) (in BGR order).
         Returns:
-            predictions (dict):
+            predictions (dict or List[dict]):
                 the output of the model for one image only.
                 See :doc:`/tutorials/models` for details about the format.
         """
         with torch.no_grad():  # https://github.com/sphinx-doc/sphinx/issues/4258
-            # Apply pre-processing to image.
-            if self.input_format == "RGB":
-                # whether the model expects BGR inputs or RGB
-                original_image = original_image[:, :, ::-1]
-            height, width = original_image.shape[:2]
-            image = self.aug.get_transform(original_image).apply_image(original_image)
-            image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
-            image.to(self.cfg.MODEL.DEVICE)
-
-            inputs = {"image": image, "height": height, "width": width}
-
-            predictions = self.model([inputs])[0]
-            return predictions
+            if isinstance(original_images, np.array):
+                original_image = original_images
+                # Apply pre-processing to image.
+                if self.input_format == "RGB":
+                    # whether the model expects BGR inputs or RGB
+                    original_image = original_image[:, :, ::-1]
+                height, width = original_image.shape[:2]
+                image = self.aug.get_transform(original_image).apply_image(original_image)
+                image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+                image.to(self.cfg.MODEL.DEVICE)
+
+                inputs = {"image": image, "height": height, "width": width}
+
+                predictions = self.model([inputs])[0]
+                return predictions
+            elif isinstance(original_images, list):
+                batch_inputs = []
+                for original_image in original_images:
+                    if self.input_format == "RGB":
+                        # whether the model expects BGR inputs or RGB
+                        original_image = original_image[:, :, ::-1]
+                    height, width = original_image.shape[:2]
+                    image = self.aug.get_transform(original_image).apply_image(original_image)
+                    image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+                    image.to(self.cfg.MODEL.DEVICE)
+
+                    inputs = {"image": image, "height": height, "width": width}
+                    batch_inputs.append(inputs)
+                predictions = self.model(batch_inputs)
+                return predictions
+            else:
+                return None
 
 
 class DefaultTrainer(TrainerBase):