diff --git a/nerfstudio/data/dataparsers/nerfstudio_dataparser.py b/nerfstudio/data/dataparsers/nerfstudio_dataparser.py
index 24dc456d15..2539a4aa05 100644
--- a/nerfstudio/data/dataparsers/nerfstudio_dataparser.py
+++ b/nerfstudio/data/dataparsers/nerfstudio_dataparser.py
@@ -31,6 +31,7 @@
     get_train_eval_split_all,
     get_train_eval_split_filename,
     get_train_eval_split_fraction,
+    get_train_eval_split_indices,
     get_train_eval_split_interval,
 )
 from nerfstudio.utils.io import load_from_json
@@ -59,7 +60,7 @@ class NerfstudioDataParserConfig(DataParserConfig):
     """The method to use to center the poses."""
     auto_scale_poses: bool = True
     """Whether to automatically scale the poses to fit in +/- 1 bounding box."""
-    eval_mode: Literal["fraction", "filename", "interval", "all"] = "fraction"
+    eval_mode: Literal["fraction", "filename", "interval", "all", "indices"] = "fraction"
     """
     The method to use for splitting the dataset into train and eval.
     Fraction splits based on a percentage for train and the remaining for eval.
@@ -77,6 +78,8 @@ class NerfstudioDataParserConfig(DataParserConfig):
     """Replace the unknown pixels with this color. Relevant if you have a mask but still sample everywhere."""
     load_3D_points: bool = False
     """Whether to load the 3D points from the colmap reconstruction."""
+    eval_image_indices: Tuple[int, ...] = (0,)
+    """Specifies the image indices to use during eval; if None, uses all."""
 
 
 @dataclass
@@ -212,6 +215,8 @@ def _generate_dataparser_outputs(self, split="train"):
                 i_train, i_eval = get_train_eval_split_filename(image_filenames)
             elif self.config.eval_mode == "interval":
                 i_train, i_eval = get_train_eval_split_interval(image_filenames, self.config.eval_interval)
+            elif self.config.eval_mode == "indices":
+                i_train, i_eval = get_train_eval_split_indices(image_filenames, self.config.eval_image_indices)
             elif self.config.eval_mode == "all":
                 CONSOLE.log(
                     "[yellow] Be careful with '--eval-mode=all'. If using camera optimization, the cameras may diverge in the current implementation, giving unpredictable results."
diff --git a/nerfstudio/data/utils/dataparsers_utils.py b/nerfstudio/data/utils/dataparsers_utils.py
index 0c79cbde18..7e8e7bfff9 100644
--- a/nerfstudio/data/utils/dataparsers_utils.py
+++ b/nerfstudio/data/utils/dataparsers_utils.py
@@ -99,3 +99,23 @@ def get_train_eval_split_all(image_filenames: List) -> Tuple[np.ndarray, np.ndar
     i_train = i_all
     i_eval = i_all
     return i_train, i_eval
+
+
+def get_train_eval_split_indices(
+    image_filenames: List, eval_image_indices: Tuple[int, ...]
+) -> Tuple[np.ndarray, np.ndarray]:
+    """
+    Get the train/eval split based on specified indices in the config.
+
+    Args:
+        image_filenames: list of image filenames
+        eval_image_indices: Tuple of indices to use for evaluation.
+    """
+    for idx in eval_image_indices:
+        if idx >= len(image_filenames) or idx < 0:
+            raise ValueError(f"Eval index {idx} is out of bounds for the number of images {len(image_filenames)}.")
+    all_indices = set(range(len(image_filenames)))
+    eval_indices = set(eval_image_indices)
+    train_indices = all_indices - eval_indices
+
+    return np.array(sorted(train_indices)), np.array(sorted(eval_indices))