diff --git a/scripts/evaluation/eval_utils.py b/scripts/evaluation/eval_utils.py
new file mode 100644
index 0000000..366335a
--- /dev/null
+++ b/scripts/evaluation/eval_utils.py
@@ -0,0 +1,77 @@
+import torch
+import warnings
+import torchvision
+import sys
+import pyarrow as pa
+import logging
+
+from dataclasses import dataclass, field
+from typing import Dict, Any, ClassVar, Deque, Mapping, Union
+from datasets.features.features import register_feature
+from torch.utils.tensorboard.writer import SummaryWriter
+
+logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+
+
+@dataclass
+class VideoFrame:
+    """
+    Provides a type for a dataset containing video frames.
+
+    Example:
+
+    ```python
+    data_dict = [{"image": {"path": "videos/episode_0.mp4", "timestamp": 0.3}}]
+    features = {"image": VideoFrame()}
+    Dataset.from_dict(data_dict, features=Features(features))
+    ```
+    """
+
+    pa_type: ClassVar[Any] = pa.struct({
+        "path": pa.string(),
+        "timestamp": pa.float32()
+    })
+    _type: str = field(default="VideoFrame", init=False, repr=False)
+
+    def __call__(self):
+        return self.pa_type
+
+
+with warnings.catch_warnings():
+    warnings.filterwarnings(
+        "ignore",
+        "'register_feature' is experimental and might be subject to breaking changes in the future.",
+        category=UserWarning,
+    )
+    register_feature(VideoFrame, "VideoFrame")
+
+
+def populate_queues(
+        queues: Dict[str, Deque[Any]],
+        batch: Mapping[str, Any]) -> Dict[str, Deque[Any]]:
+
+    for key in batch:
+        if key not in queues:
+            continue
+        if len(queues[key]) != queues[key].maxlen:
+            while len(queues[key]) != queues[key].maxlen:
+                queues[key].append(batch[key])
+        else:
+            queues[key].append(batch[key])
+    return queues
+
+
+def log_to_tensorboard(
+        writer: SummaryWriter,
+        data: Union[torch.Tensor, Any],
+        tag: str,
+        fps: int = 10) -> None:
+    if isinstance(data, torch.Tensor) and data.dim() == 5:
+        video = data
+        n = video.shape[0]
+        video = video.permute(2, 0, 1, 3, 4)
+        frame_grids = [torchvision.utils.make_grid(framesheet, nrow=int(n), padding=0) for framesheet in video]
+        grid = torch.stack(frame_grids, dim=0)
+        grid = (grid + 1.0) / 2.0
+        grid = grid.unsqueeze(dim=0)
+        writer.add_video(tag, grid, fps=fps)