diff --git a/requirements.txt b/requirements.txt
index 3fa3d16..142146a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,3 +5,5 @@ seaborn>=0.12
 scipy>=1.10
 scikit-learn>=1.3
 jupyter>=1.0
+tqdm>=4.66
+ipywidgets>=8.0
diff --git a/scripts/load_roi_data.py b/scripts/load_roi_data.py
index 82381b3..9af8a09 100644
--- a/scripts/load_roi_data.py
+++ b/scripts/load_roi_data.py
@@ -15,6 +15,15 @@ import pandas as pd
 
 from config import VIDEO_INFO_TSV
 
+# Reason: tqdm.auto picks the right backend automatically — Jupyter widget
+# inside a notebook, plain text on the CLI. Fall back to a no-op wrapper
+# if tqdm isn't installed so the loader still works in minimal environments.
+try:
+    from tqdm.auto import tqdm
+except ImportError:
+    def tqdm(iterable, **_kwargs):  # type: ignore[no-redef]
+        return iterable
+
 
 # Metadata columns to copy onto every tracking sample. These are the xlsx
 # fields that describe the experimental condition behind each fly/ROI.
@@ -47,7 +56,10 @@ def _open_ro(db_path: str, cache: dict) -> sqlite3.Connection | None:
     return cache[db_path]
 
 
-def load_roi_data(meta: pd.DataFrame | None = None) -> pd.DataFrame:
+def load_roi_data(
+    meta: pd.DataFrame | None = None,
+    progress: bool = True,
+) -> pd.DataFrame:
     """Load ROI tracking data joined with experimental metadata.
 
     For each row in ``meta``, reads the matching ROI table from both the
@@ -61,6 +73,8 @@ def load_roi_data(meta: pd.DataFrame | None = None) -> pd.DataFrame:
             ``all_video_info_merged.tsv``. Pass a filtered slice to load a
             subset (e.g. ``meta[meta.species == 'Melanogaster/CS']``).
             Defaults to the full TSV.
+        progress: show a tqdm progress bar (one tick per fly/ROI row).
+            Defaults to True. Set False for silent batch jobs.
 
     Returns:
         DataFrame with columns ``id, t, x, y, w, h, phi, is_inferred,
@@ -79,7 +93,24 @@ def load_roi_data(meta: pd.DataFrame | None = None) -> pd.DataFrame:
     db_cache: dict = {}
     chunks: list[pd.DataFrame] = []
 
-    for row in meta.itertuples(index=False):
+    n_rows = len(meta)
+    if progress:
+        # Reason: this is a slow operation (one SQL query per session per
+        # ROI; the full batch is ~minutes). Print up front so the user
+        # knows whether to grab a coffee.
+        print(
+            f"Loading ROI data for {n_rows} flies × 2 sessions "
+            f"({2 * n_rows} DB queries). This typically takes 1–3 minutes."
+        )
+    iterator = tqdm(
+        meta.itertuples(index=False),
+        total=n_rows,
+        desc="loading flies",
+        unit="fly",
+        disable=not progress,
+    )
+
+    for row in iterator:
         for session in ("training", "testing"):
             conn = _open_ro(getattr(row, f"{session}_db_path"), db_cache)
             if conn is None: