diff --git a/benchmark.py b/benchmark.py
new file mode 100755
index 0000000..eafbac8
--- /dev/null
+++ b/benchmark.py
@@ -0,0 +1,75 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import logging
+from pathlib import Path
+
+import mediapipe as mp
+import numpy as np
+import pandas as pd
+import torch.nn as nn
+
+from facebias import load_dataset
+from facebias.detectors import get_face_boxes
+from facebias.detectors.mediapipe import MediapipeDetector
+from facebias.estimators.fairface import FairFace
+from facebias.estimators.mivolov1 import MiVOLOv1
+from facebias.metrics import calc_metrics_per_subgroup, calc_metrics
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(f"facebias:{__file__}")
+
+
+if __name__ == "__main__":
+    import os
+    logger.info(os.getcwd())
+
+    DATASET_PATH = Path("data/facing2-train/")
+    METADATA_PATH = DATASET_PATH / "meta-w-age.csv"
+    DETECTOR_PATH = Path("models/blaze_face_full_range.tflite")
+    # TEST_IMS: list[str] = ["10", "12", "14", "9"]
+
+    detector = MediapipeDetector(str(DETECTOR_PATH))
+    imdict, meta = load_dataset(
+        DATASET_PATH, meta_path=METADATA_PATH, imname_proc_fn=lambda x: x.split("_")[0]
+    )
+    for im, feats in meta.items():
+        age = int(feats["age"])
+        d = age // 10 * 10
+        feats["age_group"] = "{}-{}".format(d, d + 9)
+        feats["age"] = float(feats["age"])
+
+    face_bboxes = get_face_boxes(imdict, detector)
+
+    # for t in TEST_IMS:
+    #     logger.info("-- {} - {}".format(t, meta[str(t)]))
+
+    print(FairFace.capabilities())
+    model_ff = FairFace(Path("models/fairface_alldata_4race_20191111.pt"), device="cpu")
+    preds_ff = model_ff.predict(imdict)
+    metrics_ff = calc_model_performance(meta, preds_ff)
+
+    # logger.info("FairFace -- Test Images")
+    # for t in TEST_IMS:
+    #     logger.info("--{} - {}".format(t, preds_ff[str(t)]))
+
+    metrics_ff_groups = calc_metrics_per_subgroup(meta, preds_ff)
+    for k, v in metrics_ff_groups.items():
+        for kv, vv in v.items():
+            print(k, kv, vv)
+
+    print(MiVOLOv1.capabilities())
+    model_mv = MiVOLOv1(
+        Path("models/volo-v1_model_imdb_age_gender_4.22.pth.tar"), device="cpu"
+    )
+    preds_mv = model_mv.predict(imdict)
+
+    # logger.info("MiVOLOv1(Face Only) -- Test Images")
+    # for t in TEST_IMS:
+    #     logger.info("{} - {}".format(t, preds_mv[str(t)]))
+
+    metrics_mv = calc_model_performance(meta, preds_mv)
+    metrics_mv_groups = calc_metrics_per_subgroup(meta, preds_mv)
+    for k, v in metrics_mv_groups.items():
+        for kv, vv in v.items():
+            print(k, kv, vv)