diff --git a/bit_ov/README.md b/bit_ov/README.md
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+++ b/bit_ov/README.md
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+
diff --git a/bit_ov/download_mo_bit_models.sh b/bit_ov/download_mo_bit_models.sh
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+#!/bin/bash
+#
+# This script will download BiT models and create OpenVINO IRs.
+#
+
+# Download BiT models.
+
+wget https://tfhub.dev/google/bit/m-r50x1/1?tf-hub-format=compressed -O bit_m_r50x1_1.tar.gz
+mkdir -p bit_m_r50x1_1 && tar -xvf bit_m_r50x1_1.tar.gz -C bit_m_r50x1_1
+
+wget https://tfhub.dev/google/bit/m-r50x3/1?tf-hub-format=compressed -O bit_m_r50x3_1.tar.gz
+mkdir -p bit_m_r50x3_1 && tar -xvf bit_m_r50x3_1.tar.gz -C bit_m_r50x3_1
+
+wget https://tfhub.dev/google/bit/m-r101x1/1?tf-hub-format=compressed -O bit_m_r101x1_1.tar.gz
+mkdir -p bit_m_r101x1_1 && tar -xvf bit_m_r101x1_1.tar.gz -C bit_m_r101x1_1
+
+wget https://tfhub.dev/google/bit/m-r101x3/1?tf-hub-format=compressed -O bit_m_r101x3_1.tar.gz
+mkdir -p bit_m_r101x3_1 && tar -xvf bit_m_r101x3_1.tar.gz -C bit_m_r101x3_1
+
+
+# Run Model Optimization and create OpenVINO IRs
+
+saved_model_dir_list=("bit_m_r50x1_1" "bit_m_r50x3_1" "bit_m_r101x1_1" "bit_m_r101x3_1")
+
+for saved_model_dir in ${saved_model_dir_list[@]}; do
+    mo --framework tf \
+    --saved_model_dir ./${saved_model_dir} \
+    --output_dir ov_irs/${saved_model_dir}/
+done
diff --git a/bit_ov/run_ov_tf_perf.py b/bit_ov/run_ov_tf_perf.py
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+++ b/bit_ov/run_ov_tf_perf.py
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+"""
+This script is tested with TensorFlow v2.9.1 and OpenVINO v2022.2.0
+Usage Example below. -tf, -ov are required, rest are optional:
+python run_ov_tf_perf.py \
+-tf https://tfhub.dev/google/bit/m-r50x1/1 \
+-ov ov_irs/bit_m_r50x1_1/saved_model.xml \
+-d CPU \
+-i https://upload.wikimedia.org/wikipedia/commons/6/6e/Golde33443.jpg \
+-s '1,128,128,3' \
+-t 10
+"""
+
+"""
+Refer below for more details:
+https://medium.com/openvino-toolkit/accelerate-big-transfer-bit-model-inference-with-intel-openvino-faaefaee8aec 
+"""
+
+import os, sys
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+import tensorflow as tf
+import tensorflow_hub as hub
+from openvino.runtime import Core
+import cv2
+import numpy as np
+import time
+import subprocess
+import argparse
+import pathlib
+from urllib.parse import urlparse
+
+# For top 5 labels.
+MAX_PREDS = 5
+args = ()
+
+
+def parse_args():
+    global args
+
+    parser = argparse.ArgumentParser(
+        description="Script to benchmark BiT model with TensorFlow and OpenVINO"
+    )
+    optional = parser._action_groups.pop()
+    required = parser.add_argument_group("required arguments")
+    required.add_argument(
+        "-tf", "--tfhub_url", help="TensorFlow HUB BiT model URL", required=True
+    )
+    required.add_argument(
+        "-ov", "--ov_xml", help="Path to OpenVINO model XML file", required=True
+    )
+    optional.add_argument(
+        "-d", "--target_device", help="Specify a target device to infer on. ", required=False,
+        default="CPU",
+    )
+    optional.add_argument(
+        "-i", "--input_image", help="Input Image URL or Path to image.", required=False,
+        default="",
+    )
+    optional.add_argument(
+        "-s", "--shape", help="Set shape for input 'N,W,H,C'. For example: '1,128,128,3' ", required=False,
+        default="1,128,128,3",
+    )
+    optional.add_argument(
+        "-t", "--bench_time", help="Benchmark duration in seconds", required=False, default=10,
+    )
+    parser._action_groups.append(optional)
+
+    return parser.parse_args()
+
+
+# Pre-process input test image
+def preprocess_image(input_image):
+
+    url_parsed = urlparse(input_image)
+    # If input_image is URL, download the image as 'test-image.'
+    if url_parsed.scheme not in (""):  # Not a local file
+        file_ext = pathlib.Path(url_parsed.path).suffix
+        inp_file_name = f"test-image{file_ext}"
+        print(f"\nDownloading input test image {input_image}")
+        output = subprocess.check_output(
+            f"curl {input_image} --output {inp_file_name}", shell=True
+        )
+    else:
+        inp_file_name = input_image
+    
+    print("\nPre-processing input image...")
+    
+    # Setup the input shape
+    [bs, w, h, c] = args.shape
+
+    if os.path.exists(inp_file_name):
+        image = cv2.imread(inp_file_name)
+        image = np.array(image)
+        img_resized = tf.image.resize(image, [w, h])
+        img_reshaped = tf.reshape(img_resized, [bs, w, h, c])
+        image = tf.image.convert_image_dtype(img_reshaped, tf.float32)
+    else:
+        print(f"Input image not found: {inp_file_name}. Initializing with random image of size {args.shape}." )
+        image = np.random.random(size=(bs, w, h, c))
+
+    return image
+
+
+def benchmark_tf(test_image):
+    # Load model into KerasLayer
+    tf_module = hub.KerasLayer(args.tfhub_url)
+
+    latency_arr = []
+    end = time.time() + int(args.bench_time)
+
+    print(f"\n==== Benchmarking TensorFlow inference for {args.bench_time}sec on {args.target_device} ====")
+    print(f"Input shape: {test_image.shape}")
+    print(f"Model: {args.tfhub_url} ")
+
+    while time.time() < end:
+        start_time = time.time()
+        tf_result = tf_module(test_image)
+        latency = time.time() - start_time
+        latency_arr.append(latency)
+
+    tf_result = tf.reshape(tf_result, [-1])
+    top5_label_idx = np.argsort(tf_result)[-MAX_PREDS::][::-1]
+
+    avg_latency = np.array(latency_arr).mean()
+    fps = 1 / avg_latency
+
+    print(f"Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}")
+    print(f"TF Inference top5 label index(s): {top5_label_idx}")
+
+    return avg_latency, fps, top5_label_idx
+
+
+def benchmark_ov(test_image):
+    # Initialize the OV inference engine
+    ie = Core()
+
+    # Load and compile the OV model
+    ov_model = ie.read_model(args.ov_xml)
+    compiled_model = ie.compile_model(model=ov_model, device_name=args.target_device)
+
+    # get the names of input and output layers of the model
+    input_layer = compiled_model.input(0)
+    output_layer = compiled_model.output(0)
+
+    latency_arr = []
+    end = time.time() + int(args.bench_time)
+    print(f"\n==== Benchmarking OpenVINO inference for {args.bench_time}sec on {args.target_device} ====")
+    print(f"Input shape: {test_image.shape}")
+    print(f"Model: {args.ov_xml} ")
+
+    while time.time() < end:
+        start_time = time.time()
+        ov_result = compiled_model([test_image])
+        latency = time.time() - start_time
+        latency_arr.append(latency)
+
+    # Save the result for accuracy verificaiton
+    ov_result = ov_result[output_layer]
+    ov_result = np.reshape(ov_result, [-1])
+    top5_label_idx = np.argsort(ov_result)[-MAX_PREDS::][::-1]
+
+    avg_latency = np.array(latency_arr).mean()
+    fps = 1 / avg_latency
+
+    print(f"Avg Latency: {avg_latency:.4f} sec, FPS: {fps:.2f}")
+    print(f"OV Inference top5 label index(s): {top5_label_idx} \n")
+
+    return avg_latency, fps, top5_label_idx
+
+
+def main():
+    global args
+    args = parse_args()
+
+    if isinstance(args.shape, str):
+        args.shape = [int(i) for i in args.shape.split(",")]
+        if len(args.shape) != 4:
+            sys.exit( "Input shape error. Set shape 'N,W,H,C'. For example: '1,128,128,3' " )
+
+    test_image = preprocess_image(args.input_image)
+
+    ov_avg_latency, ov_fps, ov_top5_label_idx = benchmark_ov(test_image)
+    tf_avg_latency, ov_fps, tf_top5_label_idx = benchmark_tf(test_image)
+
+    acc_diff = ov_top5_label_idx - tf_top5_label_idx
+    if np.sum(acc_diff) == 0:
+        print(f"\nBoth TensorFlow and OpenVINO reported same accuracy.")
+    else:
+        print(f"\n Accuracy MISMATCHED for TensorFlow and OpenVINO !")
+
+    speedup_ov = tf_avg_latency / ov_avg_latency
+    print(f"\nSpeedup on {args.target_device} with OpenVINO: {speedup_ov:.1f}x \n")
+
+
+if __name__ == "__main__":
+    main()