diff --git a/tftrt/benchmarking-python/CHANGELOG.md b/tftrt/benchmarking-python/CHANGELOG.md
index ea417f3b5..5845993e9 100644
--- a/tftrt/benchmarking-python/CHANGELOG.md
+++ b/tftrt/benchmarking-python/CHANGELOG.md
@@ -46,6 +46,17 @@ Description of the change
 
 <!-- YOU CAN EDIT FROM HERE -->
 
+## [1.2.0] - 2022.07.31 - @DEKHTIARJonathan
+
+Setting up the benchmarking suite to allow remote upload and storage of the
+metrics and experiments.
+
+Adding arguments:
+* `--model_name`: Name of the model being executed.
+* `--model_source`: Name of the model's source that originally published the model.
+* `--experiment_name`: Name of the experiment being conducted.
+* `--upload_metrics_endpoint`: Distant endpoint being used to push metrics to.
+
 ## [1.1.0] - 2022.07.25 - @DEKHTIARJonathan
 
 Replacing all `print()` calls by `logging.<level>()` calls
diff --git a/tftrt/benchmarking-python/benchmark_args.py b/tftrt/benchmarking-python/benchmark_args.py
index 7567daebf..042602843 100644
--- a/tftrt/benchmarking-python/benchmark_args.py
+++ b/tftrt/benchmarking-python/benchmark_args.py
@@ -175,6 +175,15 @@ def __init__(self):
             "generated and used at every iteration."
         )
 
+        self._parser.add_argument(
+            "--precision",
+            type=str,
+            choices=self.ALLOWED_PRECISION_MODES,
+            default="FP32",
+            help="Precision mode to use. FP16 and INT8 modes only works if "
+            "--use_tftrt is used."
+        )
+
         # =========================== TF-TRT Flags ========================== #
 
         self._add_bool_argument(
@@ -232,15 +241,6 @@ def __init__(self):
             help="If set to True, TensorRT engines are built before runtime."
         )
 
-        self._parser.add_argument(
-            "--precision",
-            type=str,
-            choices=self.ALLOWED_PRECISION_MODES,
-            default="FP32",
-            help="Precision mode to use. FP16 and INT8 modes only works if "
-            "--use_tftrt is used."
-        )
-
         self._add_bool_argument(
             name="use_dynamic_shape",
             default=False,
@@ -248,7 +248,31 @@ def __init__(self):
             help="Whether to use implicit batch mode or dynamic shape mode."
         )
 
-        # =========================== DEBUG Flags ========================== #
+        # =========================== Metric Flags ========================== #
+
+        self._parser.add_argument(
+            "--experiment_name",
+            type=str,
+            default=None,
+            help="Name of the experiment being run, only used for archiving "
+            "objectives: exports in JSON or CSV."
+        )
+
+        self._parser.add_argument(
+            "--model_name",
+            type=str,
+            required=True,
+            default=None,
+            help="Name of the model being benchmarked."
+        )
+
+        self._parser.add_argument(
+            "--model_source",
+            type=str,
+            required=True,
+            default=None,
+            help="Source of the model where it was originally published."
+        )
 
         self._parser.add_argument(
             "--export_metrics_json_path",
@@ -266,6 +290,16 @@ def __init__(self):
             "to the set location in CSV format for further processing."
         )
 
+        self._parser.add_argument(
+            "--upload_metrics_endpoint",
+            type=str,
+            default=None,
+            help="If set, the benchmark will upload the metrics in JSON format "
+            "to the set endpoint using a PUT requests."
+        )
+
+        # =========================== TF Profiling =========================== #
+
         self._parser.add_argument(
             "--tf_profile_export_path",
             type=str,
@@ -281,6 +315,8 @@ def __init__(self):
             help="If set to True, will add extra information to the TF Profile."
         )
 
+        # ============================ Debug Flags =========================== #
+
         self._add_bool_argument(
             name="debug",
             default=False,
@@ -388,6 +424,9 @@ def _validate_args(self, args):
                         "doesn't exist or is not a directory"
                     )
 
+        if args.upload_metrics_endpoint is not None:
+            raise NotImplementedError("This feature is not yet implemented.")
+
     def _post_process_args(self, args):
         if args.use_synthetic_data:
             # This variable is not used in synthetic data mode.
diff --git a/tftrt/benchmarking-python/benchmark_info.py b/tftrt/benchmarking-python/benchmark_info.py
index c42a01571..8ba860fae 100644
--- a/tftrt/benchmarking-python/benchmark_info.py
+++ b/tftrt/benchmarking-python/benchmark_info.py
@@ -10,7 +10,7 @@
 # The `__version__` number shall be updated everytime core benchmarking files
 # are updated.
 # Please update CHANGELOG.md with a description of what this version changed.
-__version__ = "1.1.0"
+__version__ = "1.2.0"
 
 
 def get_commit_id():
diff --git a/tftrt/benchmarking-python/huggingface/bert/base_run_inference.sh b/tftrt/benchmarking-python/huggingface/bert/base_run_inference.sh
index a95cd2cad..e2a6b9f95 100755
--- a/tftrt/benchmarking-python/huggingface/bert/base_run_inference.sh
+++ b/tftrt/benchmarking-python/huggingface/bert/base_run_inference.sh
@@ -143,6 +143,8 @@ python ${BASE_DIR}/infer.py \
     --calib_data_dir ${DATA_DIR} \
     --input_saved_model_dir ${INPUT_SAVED_MODEL_DIR} \
     --output_saved_model_dir /tmp/$RANDOM \
+    --model_name "${MODEL_NAME}" \
+    --model_source "huggingface" \
     --batch_size ${BATCH_SIZE} \
     --vocab_size ${VOCAB_SIZE} \
     --sequence_length=${SEQ_LEN} \
diff --git a/tftrt/benchmarking-python/huggingface/gpt2/base_run_inference.sh b/tftrt/benchmarking-python/huggingface/gpt2/base_run_inference.sh
index 1e5f70a8c..71e890f6a 100755
--- a/tftrt/benchmarking-python/huggingface/gpt2/base_run_inference.sh
+++ b/tftrt/benchmarking-python/huggingface/gpt2/base_run_inference.sh
@@ -105,6 +105,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "${MODEL_NAME}" \
+    --model_source "huggingface" \
     --tokenizer_model_dir=${TOKENIZER_DIR} \
     --batch_size=${BATCH_SIZE} \
     --sequence_length=${SEQ_LEN} \
diff --git a/tftrt/benchmarking-python/huggingface/t5/base_run_inference.sh b/tftrt/benchmarking-python/huggingface/t5/base_run_inference.sh
index e39737869..98024e876 100755
--- a/tftrt/benchmarking-python/huggingface/t5/base_run_inference.sh
+++ b/tftrt/benchmarking-python/huggingface/t5/base_run_inference.sh
@@ -116,6 +116,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "${MODEL_NAME}" \
+    --model_source "huggingface" \
     --tokenizer_model_dir=${TOKENIZER_DIR}\
     --vocab_model_dir=${TOKENIZER_DIR}\
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
diff --git a/tftrt/benchmarking-python/image_classification/base_run_inference.sh b/tftrt/benchmarking-python/image_classification/base_run_inference.sh
index 1c4a44836..15bf02601 100755
--- a/tftrt/benchmarking-python/image_classification/base_run_inference.sh
+++ b/tftrt/benchmarking-python/image_classification/base_run_inference.sh
@@ -79,12 +79,6 @@ case ${MODEL_NAME} in
     NUM_CLASSES=1000
     ;;
 
-  "resnet50-v1.5_tf1_ngc" )
-    NUM_CLASSES=1000
-    OUTPUT_TENSORS_NAME="classes"
-    PREPROCESS_METHOD="resnet50_v1_5_tf1_ngc"
-    ;;
-
   "resnet50v2_backbone" | "resnet50v2_sparse_backbone" )
     INPUT_SIZE=256
     OUTPUT_TENSORS_NAME="outputs"
@@ -151,6 +145,8 @@ python ${BASE_DIR}/infer.py \
     --calib_data_dir ${DATA_DIR} \
     --input_saved_model_dir ${INPUT_SAVED_MODEL_DIR} \
     --output_saved_model_dir /tmp/$RANDOM \
+    --model_name "${MODEL_NAME}" \
+    --model_source "tf_models_image" \
     --input_size ${INPUT_SIZE} \
     --preprocess_method ${PREPROCESS_METHOD} \
     --num_classes ${NUM_CLASSES} \
diff --git a/tftrt/benchmarking-python/image_classification/infer.py b/tftrt/benchmarking-python/image_classification/infer.py
index ecba62ace..645d1e068 100644
--- a/tftrt/benchmarking-python/image_classification/infer.py
+++ b/tftrt/benchmarking-python/image_classification/infer.py
@@ -64,7 +64,7 @@ def __init__(self):
         self._parser.add_argument(
             '--preprocess_method',
             type=str,
-            choices=['vgg', 'inception', 'resnet50_v1_5_tf1_ngc'],
+            choices=['vgg', 'inception'],
             default='vgg',
             help='The image preprocessing method used in dataloading.'
         )
diff --git a/tftrt/benchmarking-python/image_classification/models/resnet50-v1.5_tf1_ngc/run_inference.sh b/tftrt/benchmarking-python/image_classification/models/resnet50-v1.5_tf1_ngc/run_inference.sh
deleted file mode 100755
index f01361b3f..000000000
--- a/tftrt/benchmarking-python/image_classification/models/resnet50-v1.5_tf1_ngc/run_inference.sh
+++ /dev/null
@@ -1,5 +0,0 @@
-#!/bin/bash
-
-BASE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )/../.."
-
-bash ${BASE_DIR}/base_run_inference.sh --model_name="resnet50-v1.5_tf1_ngc" ${@}
diff --git a/tftrt/benchmarking-python/nvidia_examples/bert_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/bert_tf2/run_inference.sh
index 0e2e2122f..a193b62c5 100755
--- a/tftrt/benchmarking-python/nvidia_examples/bert_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/bert_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/squad/bert_tf2 \
     --calib_data_dir=/data/squad/bert_tf2 \
     --input_saved_model_dir=/models/nvidia_examples/bert_tf2 \
+    --model_name "bert_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=64 \
     --output_tensors_name="end_positions,start_positions" \
     --total_max_samples=11000 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/dlrm_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/dlrm_tf2/run_inference.sh
index 13f01b797..4b97869b9 100755
--- a/tftrt/benchmarking-python/nvidia_examples/dlrm_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/dlrm_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/criteo \
     --calib_data_dir=/data/criteo \
     --input_saved_model_dir=/models/nvidia_examples/dlrm_tf2 \
+    --model_name "dlrm_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=65536 \
     --output_tensors_name="output_1" \
     --total_max_samples=92000000 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B0_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B0_tf2/run_inference.sh
index 4fd28349c..f3200505f 100755
--- a/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B0_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B0_tf2/run_inference.sh
@@ -12,6 +12,8 @@ python ${BASE_DIR}/infer.py \
     --calib_data_dir=/data/imagenet \
     --input_saved_model_dir=/models/nvidia_examples/efficientnet_v1_B0_tf2 \
     --batch_size=128 \
+    --model_name "efficientnet_v1_B0_tf2" \
+    --model_source "nvidia_examples" \
     --output_tensors_name="output_1" \
     --total_max_samples=55000 \
     --input_size=224 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B4_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B4_tf2/run_inference.sh
index 74569aafc..d19f692ef 100755
--- a/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B4_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/efficientnet_v1_B4_tf2/run_inference.sh
@@ -11,6 +11,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/imagenet \
     --calib_data_dir=/data/imagenet \
     --input_saved_model_dir=/models/nvidia_examples/efficientnet_v1_B4_tf2 \
+    --model_name "efficientnet_v1_B4_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=128 \
     --output_tensors_name="output_1" \
     --total_max_samples=55000 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/efficientnet_v2_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/efficientnet_v2_tf2/run_inference.sh
index ba5115207..725c1446a 100755
--- a/tftrt/benchmarking-python/nvidia_examples/efficientnet_v2_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/efficientnet_v2_tf2/run_inference.sh
@@ -11,6 +11,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/imagenet \
     --calib_data_dir=/data/imagenet \
     --input_saved_model_dir=/models/nvidia_examples/efficientnet_v2_tf2 \
+    --model_name "efficientnet_v2_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=128 \
     --output_tensors_name="output_1" \
     --total_max_samples=55000 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/electra_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/electra_tf2/run_inference.sh
index 72f7dcd71..5d41307c3 100755
--- a/tftrt/benchmarking-python/nvidia_examples/electra_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/electra_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/squad/electra \
     --calib_data_dir=/data/squad/electra \
     --input_saved_model_dir=/models/nvidia_examples/electra_tf2 \
+    --model_name "electra_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=64 \
     --do_lower_case \
     --output_tensors_name="tf_electra_for_question_answering,tf_electra_for_question_answering_1,tf_electra_for_question_answering_2,tf_electra_for_question_answering_3,tf_electra_for_question_answering_4" \
diff --git a/tftrt/benchmarking-python/nvidia_examples/mrcnn_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/mrcnn_tf2/run_inference.sh
index 83561204f..02671fbf2 100755
--- a/tftrt/benchmarking-python/nvidia_examples/mrcnn_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/mrcnn_tf2/run_inference.sh
@@ -17,6 +17,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/coco2017/tfrecord \
     --calib_data_dir=/data/coco2017/tfrecord \
     --input_saved_model_dir=/models/nvidia_examples/mrcnn_tf2 \
+    --model_name "mrcnn_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=8 \
     --output_tensors_name="detection_boxes,detection_classes,detection_scores,image_info,num_detections,source_ids" \
     --total_max_samples=5200 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/nnunet2d_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/nnunet2d_tf2/run_inference.sh
index 24354dfd1..0699f1314 100755
--- a/tftrt/benchmarking-python/nvidia_examples/nnunet2d_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/nnunet2d_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/msd_task01/numpy_2d_bigger_tf2 \
     --calib_data_dir=/data/msd_task01/numpy_2d_bigger_tf2 \
     --input_saved_model_dir=/models/nvidia_examples/nnunet2d_tf2 \
+    --model_name "nnunet2d_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=1 \
     --num_image_slices=32 \
     --output_tensors_name="output_1" \
diff --git a/tftrt/benchmarking-python/nvidia_examples/nnunet3d_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/nnunet3d_tf2/run_inference.sh
index 273c0812f..a41f79e6b 100755
--- a/tftrt/benchmarking-python/nvidia_examples/nnunet3d_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/nnunet3d_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/msd_task01/numpy_3d_bigger_tf2 \
     --calib_data_dir=/data/msd_task01/numpy_3d_bigger_tf2 \
     --input_saved_model_dir=/models/nvidia_examples/nnunet3d_tf2 \
+    --model_name "nnunet3d_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=1 \
     --output_tensors_name="output_1" \
     --total_max_samples=500 \
diff --git a/tftrt/benchmarking-python/image_classification/models/resnet50-v1.5_tf1_ngc/analysis.txt b/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/analysis.txt
similarity index 100%
rename from tftrt/benchmarking-python/image_classification/models/resnet50-v1.5_tf1_ngc/analysis.txt
rename to tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/analysis.txt
diff --git a/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/infer.py b/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/infer.py
new file mode 100644
index 000000000..5f0fc9134
--- /dev/null
+++ b/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/infer.py
@@ -0,0 +1,165 @@
+# Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+import os
+import sys
+
+import numpy as np
+
+import tensorflow as tf
+
+# Allow import of top level python files
+import inspect
+
+currentdir = os.path.dirname(
+    os.path.abspath(inspect.getfile(inspect.currentframe()))
+)
+
+benchmark_base_dir = os.path.dirname(os.path.dirname(currentdir))
+sys.path.insert(0, benchmark_base_dir)
+sys.path.insert(0, os.path.join(benchmark_base_dir, "image_classification"))
+
+from benchmark_args import BaseCommandLineAPI
+from benchmark_runner import BaseBenchmarkRunner
+
+from image_classification.dataloading import get_dataloader
+from image_classification import preprocessing
+
+
+class CommandLineAPI(BaseCommandLineAPI):
+
+    def __init__(self):
+        super(CommandLineAPI, self).__init__()
+
+        self._parser.add_argument(
+            '--input_size',
+            type=int,
+            default=224,
+            help='Size of input images expected by the '
+            'model'
+        )
+
+        self._parser.add_argument(
+            '--num_classes',
+            type=int,
+            default=1000,
+            help='Number of classes used when training '
+            'the model'
+        )
+
+        self._parser.add_argument(
+            '--preprocess_method',
+            type=str,
+            choices=['resnet50_v1_5_tf1_ngc'],
+            default='resnet50_v1_5_tf1_ngc',
+            help='The image preprocessing method used in dataloading.'
+        )
+
+    def _post_process_args(self, args):
+        args = super(CommandLineAPI, self)._post_process_args(args)
+        args.labels_shift = 1 if args.num_classes == 1001 else 0
+
+        return args
+
+    def _validate_args(self, args):
+        super(CommandLineAPI, self)._validate_args(args)
+
+        if args.input_size != 224:
+            raise ValueError(
+                "The argument --input_size must be equal to 224 for this model."
+            )
+
+        if args.num_classes != 1000:
+            raise ValueError(
+                "The argument --num_classes must be equal to 1000 for this model."
+            )
+
+
+class BenchmarkRunner(BaseBenchmarkRunner):
+
+    def get_dataset_batches(self):
+        """Returns a list of batches of input samples.
+
+        Each batch should be in the form [x, y], where
+        x is a numpy array of the input samples for the batch, and
+        y is a numpy array of the expected model outputs for the batch
+
+        Returns:
+        - dataset: a TF Dataset object
+        - bypass_data_to_eval: any object type that will be passed unmodified to
+                            `evaluate_result()`. If not necessary: `None`
+
+        Note: script arguments can be accessed using `self._args.attr`
+        """
+
+        dataset = get_dataloader(self._args)
+
+        return dataset, None
+
+    def preprocess_model_inputs(self, data_batch):
+        """This function prepare the `data_batch` generated from the dataset.
+        Returns:
+            x: input of the model
+            y: data to be used for model evaluation
+
+        Note: script arguments can be accessed using `self._args.attr`
+        """
+
+        x, y = data_batch
+        return x, y
+
+    def postprocess_model_outputs(self, predictions, expected):
+        """Post process if needed the predictions and expected tensors. At the
+        minimum, this function transforms all TF Tensors into a numpy arrays.
+        Most models will not need to modify this function.
+
+        Note: script arguments can be accessed using `self._args.attr`
+        """
+
+        predictions = predictions.numpy()
+
+        if len(predictions.shape) != 1:
+            predictions = tf.math.argmax(predictions, axis=1)
+            predictions = predictions.numpy().reshape(-1)
+
+        predictions - self._args.labels_shift
+
+        return predictions - self._args.labels_shift, expected.numpy()
+
+    def evaluate_model(self, predictions, expected, bypass_data_to_eval):
+        """Evaluate result predictions for entire dataset.
+
+        This computes overall accuracy, mAP,  etc.  Returns the
+        metric value and a metric_units string naming the metric.
+
+        Note: script arguments can be accessed using `self._args.attr`
+        """
+
+        return (
+            np.mean(predictions["data"] == expected["data"]) * 100.0,
+            "Top-1 Accuracy %"
+        )
+
+
+if __name__ == '__main__':
+
+    cmdline_api = CommandLineAPI()
+    args = cmdline_api.parse_args()
+
+    runner = BenchmarkRunner(args)
+
+    runner.execute_benchmark()
diff --git a/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/run_inference.sh
new file mode 100755
index 000000000..1ad008ed6
--- /dev/null
+++ b/tftrt/benchmarking-python/nvidia_examples/resnet50-v1.5_tf1/run_inference.sh
@@ -0,0 +1,89 @@
+#!/bin/bash
+
+nvidia-smi
+
+BASE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )"
+
+# Default Argument Values
+BATCH_SIZE=128
+OUTPUT_TENSOR_NAMES="classes"
+TOTAL_MAX_SAMPLES=50000
+DATA_DIR="/data/imagenet"
+MODEL_DIR="/models/nvidia_examples/resnet50-v1.5_tf1"
+
+BYPASS_ARGUMENTS=""
+
+# Loop through arguments and process them
+for arg in "$@"
+do
+    case $arg in
+        --model_name=*)
+        MODEL_NAME="${arg#*=}"
+        shift # Remove --model_name from processing
+        ;;
+        --batch_size=*)
+        BATCH_SIZE="${arg#*=}"
+        shift # Remove --batch_size= from processing
+        ;;
+        --output_tensors_name=*)
+        OUTPUT_TENSOR_NAMES="${arg#*=}"
+        shift # Remove --output_tensors_name= from processing
+        ;;
+        --data_dir=*)
+        DATA_DIR="${arg#*=}"
+        shift # Remove --data_dir= from processing
+        ;;
+        --input_saved_model_dir=*)
+        MODEL_DIR="${arg#*=}"
+        shift # Remove --input_saved_model_dir= from processing
+        ;;
+        --total_max_samples=*)
+        TOTAL_MAX_SAMPLES="${arg#*=}"
+        shift # Remove --total_max_samples= from processing
+        ;;
+        *)
+        BYPASS_ARGUMENTS="${BYPASS_ARGUMENTS} ${arg}"
+        ;;
+    esac
+done
+
+# Trimming front and back whitespaces
+BYPASS_ARGUMENTS=$(echo ${BYPASS_ARGUMENTS} | tr -s " ")
+
+echo -e "\n********************************************************************"
+echo ""
+echo "[*] DATA_DIR: ${DATA_DIR}"
+echo "[*] MODEL_DIR: ${MODEL_DIR}"
+echo ""
+echo "[*] BATCH_SIZE: ${BATCH_SIZE}"
+echo "[*] OUTPUT_TENSOR_NAMES: ${OUTPUT_TENSOR_NAMES}"
+echo ""
+echo "[*] BYPASS_ARGUMENTS: ${BYPASS_ARGUMENTS}"
+
+echo -e "********************************************************************\n"
+
+if [[ ! -d ${DATA_DIR} ]]; then
+    echo "ERROR: \`--data_dir=/path/to/directory\` does not exist. [Received: \`${DATA_DIR}\`]"
+    exit 1
+fi
+
+if [[ ! -d ${MODEL_DIR} ]]; then
+    echo "ERROR: \`--input_saved_model_dir=/path/to/directory\` does not exist. [Received: \`${MODEL_DIR}\`]"
+    exit 1
+fi
+
+set -x
+
+python ${BASE_DIR}/infer.py \
+    --data_dir=${DATA_DIR} \
+    --calib_data_dir=${DATA_DIR} \
+    --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "resnet50-v1.5_tf1" \
+    --model_source "nvidia_examples" \
+    --batch_size=${BATCH_SIZE} \
+    --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
+    --input_size 224 \
+    --num_classes 1000 \
+    --preprocess_method resnet50_v1_5_tf1_ngc \
+    --total_max_samples=${TOTAL_MAX_SAMPLES} \
+    ${BYPASS_ARGUMENTS}
diff --git a/tftrt/benchmarking-python/nvidia_examples/sim_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/sim_tf2/run_inference.sh
index 1b8b644a4..0515faeda 100755
--- a/tftrt/benchmarking-python/nvidia_examples/sim_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/sim_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/seq_20 \
     --calib_data_dir=/data/seq_20 \
     --input_saved_model_dir=/models/nvidia_examples/sim_tf2/21.10.0_fp32 \
+    --model_name "sim_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=256 \
     --output_tensors_name="sim_model,sim_model_1" \
     --total_max_samples=220000 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/unet_medical_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/unet_medical_tf2/run_inference.sh
index 15c4cf670..9006a9423 100755
--- a/tftrt/benchmarking-python/nvidia_examples/unet_medical_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/unet_medical_tf2/run_inference.sh
@@ -10,6 +10,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/em_segmentation \
     --calib_data_dir=/data/em_segmentation \
     --input_saved_model_dir=/models/nvidia_examples/unet_medical_tf2 \
+    --model_name "unet_medical_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=8 \
     --output_tensors_name="output_1" \
     --total_max_samples=6500 \
diff --git a/tftrt/benchmarking-python/nvidia_examples/wide_deep_tf2/run_inference.sh b/tftrt/benchmarking-python/nvidia_examples/wide_deep_tf2/run_inference.sh
index 9d3c81aba..cc2eeadd6 100755
--- a/tftrt/benchmarking-python/nvidia_examples/wide_deep_tf2/run_inference.sh
+++ b/tftrt/benchmarking-python/nvidia_examples/wide_deep_tf2/run_inference.sh
@@ -16,6 +16,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=/data/outbrain \
     --calib_data_dir=/data/outbrain \
     --input_saved_model_dir=/models/nvidia_examples/wide_deep_tf2 \
+    --model_name "wide_deep_tf2" \
+    --model_source "nvidia_examples" \
     --batch_size=32768 \
     --output_tensors_name "output_1" \
     --total_max_samples=28000000 \
diff --git a/tftrt/benchmarking-python/object_detection/base_run_inference.sh b/tftrt/benchmarking-python/object_detection/base_run_inference.sh
index fb80dd6b8..03d0cff69 100755
--- a/tftrt/benchmarking-python/object_detection/base_run_inference.sh
+++ b/tftrt/benchmarking-python/object_detection/base_run_inference.sh
@@ -147,6 +147,8 @@ python ${BASE_DIR}/infer.py \
     --annotation_path ${ANNOTATIONS_DATA_FILE} \
     --input_saved_model_dir ${INPUT_SAVED_MODEL_DIR} \
     --output_saved_model_dir /tmp/$RANDOM \
+    --model_name "${MODEL_NAME}" \
+    --model_source "tf_models_object" \
     --batch_size ${BATCH_SIZE} \
     --input_size ${INPUT_SIZE} \
     --max_workspace_size ${MAX_WORKSPACE_SIZE} \
diff --git a/tftrt/benchmarking-python/tf_hub/albert/base_run_inference.sh b/tftrt/benchmarking-python/tf_hub/albert/base_run_inference.sh
index 9cdf6ae4a..3afec0190 100755
--- a/tftrt/benchmarking-python/tf_hub/albert/base_run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/albert/base_run_inference.sh
@@ -111,6 +111,8 @@ python ${BASE_DIR}/infer.py \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
     --tokenizer_dir=${TOKENIZER_DIR}\
+    --model_name "${MODEL_NAME}" \
+    --model_source "tf_hub" \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
     `# The following is set because we will be running synthetic benchmarks` \
     --total_max_samples=1 \
diff --git a/tftrt/benchmarking-python/tf_hub/electra/base_run_inference.sh b/tftrt/benchmarking-python/tf_hub/electra/base_run_inference.sh
index dd8fce722..fede9752f 100755
--- a/tftrt/benchmarking-python/tf_hub/electra/base_run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/electra/base_run_inference.sh
@@ -108,10 +108,12 @@ set -x
 python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
+    --tokenizer_dir=${TOKENIZER_DIR}\
+    --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "${MODEL_NAME}" \
+    --model_source "tf_hub" \
     --sequence_length=${SEQ_LEN} \
     --vocab_size=${VOCAB_SIZE} \
-    --input_saved_model_dir=${MODEL_DIR} \
-    --tokenizer_dir=${TOKENIZER_DIR}\
     --batch_size=${BATCH_SIZE} \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
     --total_max_samples=${TOTAL_MAX_SAMPLES} \
diff --git a/tftrt/benchmarking-python/tf_hub/movinet/base_run_inference.sh b/tftrt/benchmarking-python/tf_hub/movinet/base_run_inference.sh
index e5d711798..b51ef3e4b 100755
--- a/tftrt/benchmarking-python/tf_hub/movinet/base_run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/movinet/base_run_inference.sh
@@ -94,6 +94,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "movinet_${MODEL_NAME}" \
+    --model_source "tf_hub" \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
     `# The following is set because we will be running synthetic benchmarks` \
     --total_max_samples=1 \
diff --git a/tftrt/benchmarking-python/tf_hub/spice/run_inference.sh b/tftrt/benchmarking-python/tf_hub/spice/run_inference.sh
index cc849f746..60f614ca1 100755
--- a/tftrt/benchmarking-python/tf_hub/spice/run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/spice/run_inference.sh
@@ -77,6 +77,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "spice" \
+    --model_source "tf_hub" \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
     --batch_size=${BATCH_SIZE} \
     --samples_per_input=${SAMPLES_PER_INPUT} \
diff --git a/tftrt/benchmarking-python/tf_hub/swin_transformers/base_run_inference.sh b/tftrt/benchmarking-python/tf_hub/swin_transformers/base_run_inference.sh
index 7c1d0e859..8731974dc 100755
--- a/tftrt/benchmarking-python/tf_hub/swin_transformers/base_run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/swin_transformers/base_run_inference.sh
@@ -90,6 +90,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "${MODEL_NAME}" \
+    --model_source "tf_hub" \
     --input_size=${INPUT_SIZE} \
     --batch_size=${BATCH_SIZE} \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
diff --git a/tftrt/benchmarking-python/tf_hub/vision_transformer/base_run_inference.sh b/tftrt/benchmarking-python/tf_hub/vision_transformer/base_run_inference.sh
index 4f029305b..21b09a1bf 100755
--- a/tftrt/benchmarking-python/tf_hub/vision_transformer/base_run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/vision_transformer/base_run_inference.sh
@@ -82,6 +82,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "${MODEL_NAME}" \
+    --model_source "tf_hub" \
     --batch_size=${BATCH_SIZE} \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
     --total_max_samples=${TOTAL_MAX_SAMPLES} \
diff --git a/tftrt/benchmarking-python/tf_hub/vision_transformer/models/vit_r26_s32_lightaug_classification/run_inference.sh b/tftrt/benchmarking-python/tf_hub/vision_transformer/models/vit_r26_s32_lightaug_classification/run_inference.sh
deleted file mode 100755
index 2077a83db..000000000
--- a/tftrt/benchmarking-python/tf_hub/vision_transformer/models/vit_r26_s32_lightaug_classification/run_inference.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-
-set -x
-
-BASE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )/../.."
-
-bash ${BASE_DIR}/base_run_inference.sh --model_name="vit_r26_s32_lightaug_classification" ${@}
\ No newline at end of file
diff --git a/tftrt/benchmarking-python/tf_hub/vision_transformer/models/vit_r26_s32_medaug_classification/run_inference.sh b/tftrt/benchmarking-python/tf_hub/vision_transformer/models/vit_r26_s32_medaug_classification/run_inference.sh
deleted file mode 100755
index 3aa4f2c33..000000000
--- a/tftrt/benchmarking-python/tf_hub/vision_transformer/models/vit_r26_s32_medaug_classification/run_inference.sh
+++ /dev/null
@@ -1,7 +0,0 @@
-#!/bin/bash
-
-set -x
-
-BASE_DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )/../.."
-
-bash ${BASE_DIR}/base_run_inference.sh --model_name="vit_r26_s32_medaug_classification" ${@}
\ No newline at end of file
diff --git a/tftrt/benchmarking-python/tf_hub/yamnet/run_inference.sh b/tftrt/benchmarking-python/tf_hub/yamnet/run_inference.sh
index e124e125f..98092fe1e 100755
--- a/tftrt/benchmarking-python/tf_hub/yamnet/run_inference.sh
+++ b/tftrt/benchmarking-python/tf_hub/yamnet/run_inference.sh
@@ -77,6 +77,8 @@ python ${BASE_DIR}/infer.py \
     --data_dir=${DATA_DIR} \
     --calib_data_dir=${DATA_DIR} \
     --input_saved_model_dir=${MODEL_DIR} \
+    --model_name "yamnet" \
+    --model_source "tf_hub" \
     --output_tensors_name=${OUTPUT_TENSOR_NAMES} \
     --batch_size=${BATCH_SIZE} \
     --frame_length=${FRAME_LENGTH} \