diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 000000000..2688d24bc
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,6 @@
+[submodule "third_party/models"]
+	path = tftrt/examples/object_detection/third_party/models
+	url = https://github.com/tensorflow/models
+[submodule "third_party/cocoapi"]
+	path = tftrt/examples/object_detection/third_party/cocoapi
+	url = https://github.com/cocodataset/cocoapi
diff --git a/README.md b/README.md
index 57e3269b2..90394bad2 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,10 @@
-Coming soon: Examples using [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt) in TensorFlow.
+# TensorRT Integration in TensorFlow
+
+This repository demonstrates TensorRT integration in TensorFlow.  Currently
+it contains examples for accelerated image classification and object 
+detection.
+
+<a name="ex"></a>
+## Examples
+
+* [Object Detection](tftrt/examples/object_detection)
diff --git a/setup.py b/setup.py
new file mode 100644
index 000000000..a727bc46e
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,27 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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.
+# =============================================================================
+
+from setuptools import find_packages, setup
+
+setup(
+    name='tftrt',
+    version='0.0',
+    description='NVIDIA TensorRT integration in TensorFlow',
+    author='NVIDIA',
+    packages=find_packages(),
+    install_requires=['tqdm']
+)
diff --git a/tftrt/__init__.py b/tftrt/__init__.py
new file mode 100644
index 000000000..04285a017
--- /dev/null
+++ b/tftrt/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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.
+# =============================================================================
diff --git a/tftrt/examples/__init__.py b/tftrt/examples/__init__.py
new file mode 100644
index 000000000..04285a017
--- /dev/null
+++ b/tftrt/examples/__init__.py
@@ -0,0 +1,16 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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.
+# =============================================================================
diff --git a/tftrt/examples/object_detection/README.md b/tftrt/examples/object_detection/README.md
new file mode 100644
index 000000000..89dead0d2
--- /dev/null
+++ b/tftrt/examples/object_detection/README.md
@@ -0,0 +1,138 @@
+# TensorRT / TensorFlow Object Detection
+
+This package demonstrated object detection using TensorRT integration in TensorFlow. 
+It includes utilities for accuracy and performance benchmarking, along with 
+utilities for model construction and optimization.
+
+* [Setup](#setup)
+* [Download](#od_download)
+* [Optimize](#od_optimize)
+* [Benchmark](#od_benchmark)
+* [Test](#od_test)
+
+<a name="setup"></a>
+## Setup
+
+1. Install object detection dependencies (from tftrt/examples/object_detection)
+
+```bash
+git submodule update --init
+./install_dependencies.sh
+```
+
+2. Ensure you've installed the tftrt package (from root folder of repository)
+
+```bash
+python setup.py install --user
+```
+
+<a name="od"></a>
+## Object Detection
+
+<a name="od_download"></a>
+### Download
+```python
+from tftrt.examples.object_detection import download_model
+
+config_path, checkpoint_path = download_model('ssd_mobilenet_v1_coco', output_dir='models')
+# help(download_model) for more
+```
+
+<a name="od_optimize"></a>
+### Optimize
+
+```python
+from tftrt.examples.object_detection import optimize_model
+
+frozen_graph = optimize_model(
+    config_path=config_path, 
+    checkpoint_path=checkpoint_path,
+    use_trt=True,
+    precision_mode='FP16'
+)
+# help(optimize_model) for other parameters
+```
+
+<a name="od_benchmark"></a>
+### Benchmark
+
+First, we download the validation dataset
+
+```python
+from tftrt.examples.object_detection import download_dataset
+
+images_dir, annotation_path = download_dataset('val2014', output_dir='dataset')
+# help(download_dataset) for more
+```
+
+Next, we run inference over the dataset to benchmark the optimized model
+
+```python
+from tftrt.examples.object_detection import benchmark_model
+
+statistics = benchmark_model(
+    frozen_graph=frozen_graph, 
+    images_dir=images_dir, 
+    annotation_path=annotation_path
+)
+# help(benchmark_model) for more parameters
+```
+
+<a name="od_test"></a>
+### Test
+To simplify evaluation of different models with different optimization parameters
+we include a ``test`` function that ingests a JSON file containing test arguments
+and combines the model download, optimization, and benchmark steps.  Below is an
+example JSON file, call it ``my_test.json``
+
+```json
+{
+  "source_model": {
+    "model_name": "ssd_inception_v2_coco",
+    "output_dir": "models"
+  },
+  "optimization_config": {
+    "use_trt": true,
+    "precision_mode": "FP16",
+    "force_nms_cpu": true,
+    "replace_relu6": true,
+    "remove_assert": true,
+    "override_nms_score_threshold": 0.3,
+    "max_batch_size": 1
+  },
+  "benchmark_config": {
+    "images_dir": "coco/val2017",
+    "annotation_path": "coco/annotations/instances_val2017.json",
+    "batch_size": 1,
+    "image_shape": [600, 600],
+    "num_images": 4096,
+    "output_path": "stats/ssd_inception_v2_coco_trt_fp16.json"
+  },
+  "assertions": [
+    "statistics['map'] > (0.268 - 0.005)"
+  ]
+}
+```
+
+We execute the test using the ``test`` python function
+
+```python
+from tftrt.examples.object_detection import test
+
+test('my_test.json')
+# help(test) for more details
+```
+
+Alternatively, we can directly call the object_detection.test module, which
+is configured to execute this function by default.
+
+```shell
+python -m tftrt.examples.object_detection.test my_test.json
+```
+
+For the example configuration shown above, the following steps will be performed
+
+1. Downloads ssd_inception_v2_coco
+2. Optimizes with TensorRT and FP16 precision
+3. Benchmarks against the MSCOCO 2017 validation dataset
+4. Asserts that the MAP is greater than some reference value
diff --git a/tftrt/examples/object_detection/__init__.py b/tftrt/examples/object_detection/__init__.py
new file mode 100644
index 000000000..d7675e24e
--- /dev/null
+++ b/tftrt/examples/object_detection/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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.
+# =============================================================================
+
+from .object_detection import download_model, download_dataset, optimize_model, benchmark_model
+from .test import test
diff --git a/tftrt/examples/object_detection/graph_utils.py b/tftrt/examples/object_detection/graph_utils.py
new file mode 100644
index 000000000..775127abb
--- /dev/null
+++ b/tftrt/examples/object_detection/graph_utils.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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 tensorflow as tf
+
+
+def make_const6(const6_name='const6'):
+    graph = tf.Graph()
+    with graph.as_default():
+        tf_6 = tf.constant(dtype=tf.float32, value=6.0, name=const6_name)
+    return graph.as_graph_def()
+
+
+def make_relu6(output_name, input_name, const6_name='const6'):
+    graph = tf.Graph()
+    with graph.as_default():
+        tf_x = tf.placeholder(tf.float32, [10, 10], name=input_name)
+        tf_6 = tf.constant(dtype=tf.float32, value=6.0, name=const6_name)
+        with tf.name_scope(output_name):
+            tf_y1 = tf.nn.relu(tf_x, name='relu1')
+            tf_y2 = tf.nn.relu(tf.subtract(tf_x, tf_6, name='sub1'), name='relu2')
+
+            #tf_y = tf.nn.relu(tf.subtract(tf_6, tf.nn.relu(tf_x, name='relu1'), name='sub'), name='relu2')
+        #tf_y = tf.subtract(tf_6, tf_y, name=output_name)
+        tf_y = tf.subtract(tf_y1, tf_y2, name=output_name)
+        
+    graph_def = graph.as_graph_def()
+    graph_def.node[-1].name = output_name
+
+    # remove unused nodes
+    for node in graph_def.node:
+        if node.name == input_name:
+            graph_def.node.remove(node)
+    for node in graph_def.node:
+        if node.name == const6_name:
+            graph_def.node.remove(node)
+    for node in graph_def.node:
+        if node.op == '_Neg':
+            node.op = 'Neg'
+            
+    return graph_def
+
+
+def convert_relu6(graph_def, const6_name='const6'):
+    # add constant 6
+    has_const6 = False
+    for node in graph_def.node:
+        if node.name == const6_name:
+            has_const6 = True
+    if not has_const6:
+        const6_graph_def = make_const6(const6_name=const6_name)
+        graph_def.node.extend(const6_graph_def.node)
+        
+    for node in graph_def.node:
+        if node.op == 'Relu6':
+            input_name = node.input[0]
+            output_name = node.name
+            relu6_graph_def = make_relu6(output_name, input_name, const6_name=const6_name)
+            graph_def.node.remove(node)
+            graph_def.node.extend(relu6_graph_def.node)
+            
+    return graph_def
+
+
+def remove_node(graph_def, node):
+    for n in graph_def.node:
+        if node.name in n.input:
+            n.input.remove(node.name)
+        ctrl_name = '^' + node.name
+        if ctrl_name in n.input:
+            n.input.remove(ctrl_name)
+    graph_def.node.remove(node)
+
+
+def remove_op(graph_def, op_name):
+    matches = [node for node in graph_def.node if node.op == op_name]
+    for match in matches:
+        remove_node(graph_def, match)
+
+
+def force_nms_cpu(frozen_graph):
+    for node in frozen_graph.node:
+        if 'NonMaxSuppression' in node.name:
+            node.device = '/device:CPU:0'
+    return frozen_graph
+
+
+def replace_relu6(frozen_graph):
+    return convert_relu6(frozen_graph)
+
+
+def remove_assert(frozen_graph):
+    remove_op(frozen_graph, 'Assert')
+    return frozen_graph
diff --git a/tftrt/examples/object_detection/install_dependencies.sh b/tftrt/examples/object_detection/install_dependencies.sh
new file mode 100755
index 000000000..0f55d90db
--- /dev/null
+++ b/tftrt/examples/object_detection/install_dependencies.sh
@@ -0,0 +1,71 @@
+#!/bin/bash
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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.
+# =============================================================================
+
+TF_MODELS_DIR=third_party/models
+COCO_API_DIR=third_party/cocoapi
+
+python -V 2>&1 | grep "Python 3" || \
+  ( export DEBIAN_FRONTEND=noninteractive && \
+    apt-get update && \
+    apt-get install -y --no-install-recommends python-tk )
+
+RESEARCH_DIR=$TF_MODELS_DIR/research
+SLIM_DIR=$RESEARCH_DIR/slim
+PYCOCO_DIR=$COCO_API_DIR/PythonAPI
+
+pushd $RESEARCH_DIR
+
+# GET PROTOC 3.5
+
+BASE_URL="https://github.com/google/protobuf/releases/download/v3.5.1/"
+PROTOC_DIR=protoc
+PROTOC_EXE=$PROTOC_DIR/bin/protoc
+
+mkdir -p $PROTOC_DIR
+pushd $PROTOC_DIR
+ARCH=$(uname -m)
+if [ "$ARCH" == "aarch64" ] ; then
+  filename="protoc-3.5.1-linux-aarch_64.zip"
+elif [ "$ARCH" == "x86_64" ] ; then
+  filename="protoc-3.5.1-linux-x86_64.zip"
+else
+  echo ERROR: $ARCH not supported.
+  exit 1;
+fi
+wget --no-check-certificate ${BASE_URL}${filename}
+unzip ${filename}
+popd
+
+# BUILD PROTOBUF FILES
+$PROTOC_EXE object_detection/protos/*.proto --python_out=.
+
+# INSTALL OBJECT DETECTION
+
+pip install -e .
+
+popd
+
+pushd $SLIM_DIR
+pip install -e .
+popd
+
+# INSTALL PYCOCOTOOLS
+
+pushd $PYCOCO_DIR
+pip install -e .
+popd
diff --git a/tftrt/examples/object_detection/object_detection.py b/tftrt/examples/object_detection/object_detection.py
new file mode 100644
index 000000000..38e86a300
--- /dev/null
+++ b/tftrt/examples/object_detection/object_detection.py
@@ -0,0 +1,632 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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.
+# =============================================================================
+
+
+from __future__ import absolute_import
+
+import tensorflow as tf
+import tensorflow.contrib.tensorrt as trt
+import tqdm
+import pdb
+
+from collections import namedtuple
+from PIL import Image
+import numpy as np
+import time
+import json
+import subprocess
+import os
+import glob
+
+from .graph_utils import force_nms_cpu as f_force_nms_cpu
+from .graph_utils import replace_relu6 as f_replace_relu6
+from .graph_utils import remove_assert as f_remove_assert
+
+from google.protobuf import text_format
+from object_detection.protos import pipeline_pb2, image_resizer_pb2
+from object_detection import exporter
+
+Model = namedtuple('Model', ['name', 'url', 'extract_dir'])
+
+INPUT_NAME = 'image_tensor'
+BOXES_NAME = 'detection_boxes'
+CLASSES_NAME = 'detection_classes'
+SCORES_NAME = 'detection_scores'
+MASKS_NAME = 'detection_masks'
+NUM_DETECTIONS_NAME = 'num_detections'
+FROZEN_GRAPH_NAME = 'frozen_inference_graph.pb'
+PIPELINE_CONFIG_NAME = 'pipeline.config'
+CHECKPOINT_PREFIX = 'model.ckpt'
+
+MODELS = {
+    'ssd_mobilenet_v1_coco':
+    Model(
+        'ssd_mobilenet_v1_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz',
+        'ssd_mobilenet_v1_coco_2018_01_28',
+    ),
+    'ssd_mobilenet_v1_0p75_depth_quantized_coco':
+    Model(
+        'ssd_mobilenet_v1_0p75_depth_quantized_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync_2018_07_18.tar.gz',
+        'ssd_mobilenet_v1_0.75_depth_quantized_300x300_coco14_sync_2018_07_18'
+    ),
+    'ssd_mobilenet_v1_ppn_coco':
+    Model(
+        'ssd_mobilenet_v1_ppn_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03.tar.gz',
+        'ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco14_sync_2018_07_03'
+    ),
+    'ssd_mobilenet_v1_fpn_coco':
+    Model(
+        'ssd_mobilenet_v1_fpn_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz',
+        'ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03'
+    ),
+    'ssd_mobilenet_v2_coco':
+    Model(
+        'ssd_mobilenet_v2_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz',
+        'ssd_mobilenet_v2_coco_2018_03_29',
+    ),
+    'ssdlite_mobilenet_v2_coco':
+    Model(
+        'ssdlite_mobilenet_v2_coco',
+        'http://download.tensorflow.org/models/object_detection/ssdlite_mobilenet_v2_coco_2018_05_09.tar.gz',
+        'ssdlite_mobilenet_v2_coco_2018_05_09'),
+    'ssd_inception_v2_coco':
+    Model(
+        'ssd_inception_v2_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2018_01_28.tar.gz',
+        'ssd_inception_v2_coco_2018_01_28',
+    ),
+    'ssd_resnet_50_fpn_coco':
+    Model(
+        'ssd_resnet_50_fpn_coco',
+        'http://download.tensorflow.org/models/object_detection/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03.tar.gz',
+        'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03',
+    ),
+    'faster_rcnn_resnet50_coco':
+    Model(
+        'faster_rcnn_resnet50_coco',
+        'http://download.tensorflow.org/models/object_detection/faster_rcnn_resnet50_coco_2018_01_28.tar.gz',
+        'faster_rcnn_resnet50_coco_2018_01_28',
+    ),
+    'faster_rcnn_nas':
+    Model(
+        'faster_rcnn_nas',
+        'http://download.tensorflow.org/models/object_detection/faster_rcnn_nas_coco_2018_01_28.tar.gz',
+        'faster_rcnn_nas_coco_2018_01_28',
+    ),
+    'mask_rcnn_resnet50_atrous_coco':
+    Model(
+        'mask_rcnn_resnet50_atrous_coco',
+        'http://download.tensorflow.org/models/object_detection/mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz',
+        'mask_rcnn_resnet50_atrous_coco_2018_01_28',
+    ),
+    'facessd_mobilenet_v2_quantized_open_image_v4':
+    Model(
+        'facessd_mobilenet_v2_quantized_open_image_v4',
+        'http://download.tensorflow.org/models/object_detection/facessd_mobilenet_v2_quantized_320x320_open_image_v4.tar.gz',
+        'facessd_mobilenet_v2_quantized_320x320_open_image_v4')
+}
+
+Dataset = namedtuple(
+    'Dataset',
+    ['images_url', 'images_dir', 'annotation_url', 'annotation_path'])
+
+DATASETS = {
+    'val2014':
+    Dataset(
+        'http://images.cocodataset.org/zips/val2014.zip', 'val2014',
+        'http://images.cocodataset.org/annotations/annotations_trainval2014.zip',
+        'annotations/instances_val2014.json'),
+    'train2014':
+    Dataset(
+        'http://images.cocodataset.org/zips/train2014.zip', 'train2014',
+        'http://images.cocodataset.org/annotations/annotations_trainval2014.zip',
+        'annotations/instances_train2014.json'),
+    'val2017':
+    Dataset(
+        'http://images.cocodataset.org/zips/val2017.zip', 'val2017',
+        'http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
+        'annotations/instances_val2017.json'),
+    'train2017':
+    Dataset(
+        'http://images.cocodataset.org/zips/train2017.zip', 'train2017',
+        'http://images.cocodataset.org/annotations/annotations_trainval2017.zip',
+        'annotations/instances_train2017.json')
+}
+
+
+def download_model(model_name, output_dir='.'):
+    """Downloads a model from the TensorFlow Object Detection API
+
+    Downloads a model from the TensorFlow Object Detection API to a specific
+    output directory.  The download will be skipped if an existing directory
+    for the selected model already found under output_dir.
+
+    Args
+    ----
+        model_name: A string representing the model to download.  This must be
+            one of the keys in the module variable
+            ``trt_samples.object_detection.MODELS``.
+        output_dir: A string representing the directory to download the model
+            under.  A directory for the specified model will be created at
+            ``output_dir/<model_directory>``.  If output_dir/<model_directory>
+            already exists, then the download will be skipped.
+
+    Returns
+    -------
+        config_path: A string representing the path to the object detection
+            pipeline configuration file of the downloaded model.
+        checkpoint_path: A string representing the path to the object detection
+            model checkpoint.
+    """
+    global MODELS
+
+    model_name
+
+    model = MODELS[model_name]
+
+    # make output directory if it doesn't exist
+    subprocess.call(['mkdir', '-p', output_dir])
+
+    tar_file = os.path.join(output_dir, os.path.basename(model.url))
+
+    config_path = os.path.join(output_dir, model.extract_dir,
+                               PIPELINE_CONFIG_NAME)
+    checkpoint_path = os.path.join(output_dir, model.extract_dir,
+                                   CHECKPOINT_PREFIX)
+
+    extract_dir = os.path.join(output_dir, model.extract_dir)
+    if os.path.exists(extract_dir):
+        print('Using cached model found at: %s' % extract_dir)
+    else:
+        subprocess.call(['wget', model.url, '-O', tar_file])
+        subprocess.call(['tar', '-xzf', tar_file, '-C', output_dir])
+
+        # hack fix to handle mobilenet_v2 config bug
+        subprocess.call(['sed', '-i', '/batch_norm_trainable/d', config_path])
+
+    return config_path, checkpoint_path
+
+
+def optimize_model(config_path,
+                   checkpoint_path,
+                   use_trt=True,
+                   force_nms_cpu=True,
+                   replace_relu6=True,
+                   remove_assert=True,
+                   override_nms_score_threshold=None,
+                   override_resizer_shape=None,
+                   max_batch_size=1,
+                   precision_mode='FP32',
+                   minimum_segment_size=50,
+                   max_workspace_size_bytes=1 << 25,
+                   calib_images_dir=None,
+                   num_calib_images=None,
+                   calib_image_shape=None,
+                   tmp_dir='.optimize_model_tmp_dir',
+                   remove_tmp_dir=True,
+                   output_path=None):
+    """Optimizes an object detection model using TensorRT
+
+    Optimizes an object detection model using TensorRT.  This method also
+    performs pre-tensorrt optimizations specific to the TensorFlow object
+    detection API models.  Please see the list of arguments for other
+    optimization parameters.
+
+    Args
+    ----
+        config_path: A string representing the path of the object detection
+            pipeline config file.
+        checkpoint_path: A string representing the path of the object
+            detection model checkpoint.
+        use_trt: A boolean representing whether to optimize with TensorRT. If
+            False, regular TensorFlow will be used but other optimizations
+            (like NMS device placement) will still be applied.
+        force_nms_cpu: A boolean indicating whether to place NMS operations on
+            the CPU.
+        replace_relu6: A boolean indicating whether to replace relu6(x)
+            operations with relu(x) - relu(x-6).
+        remove_assert: A boolean indicating whether to remove Assert
+            operations from the graph.
+        override_nms_score_threshold: An optional float representing
+            a NMS score threshold to override that specified in the object
+            detection configuration file.
+        override_resizer_shape: An optional list/tuple of integers
+            representing a fixed shape to override the default image resizer
+            specified in the object detection configuration file.
+        max_batch_size: An integer representing the max batch size to use for
+            TensorRT optimization.
+        precision_mode: A string representing the precision mode to use for
+            TensorRT optimization.  Must be one of 'FP32', 'FP16', or 'INT8'.
+        minimum_segment_size: An integer representing the minimum segment size
+            to use for TensorRT graph segmentation.
+        max_workspace_size_bytes: An integer representing the max workspace
+            size for TensorRT optimization.
+        calib_images_dir: A string representing a directory containing images to
+            use for int8 calibration. 
+        num_calib_images: An integer representing the number of calibration 
+            images to use.  If None, will use all images in directory.
+        calib_image_shape: A tuple of integers representing the height, 
+            width that images will be resized to for calibration. 
+        tmp_dir: A string representing a directory for temporary files.  This
+            directory will be created and removed by this function and should
+            not already exist.  If the directory exists, an error will be
+            thrown.
+        remove_tmp_dir: A boolean indicating whether we should remove the
+            tmp_dir or throw error.
+        output_path: An optional string representing the path to save the
+            optimized GraphDef to.
+
+    Returns
+    -------
+        A GraphDef representing the optimized model.
+    """
+    if os.path.exists(tmp_dir):
+        if not remove_tmp_dir:
+            raise RuntimeError(
+                'Cannot create temporary directory, path exists: %s' % tmp_dir)
+        subprocess.call(['rm', '-rf', tmp_dir])
+
+    # load config from file
+    config = pipeline_pb2.TrainEvalPipelineConfig()
+    with open(config_path, 'r') as f:
+        text_format.Merge(f.read(), config, allow_unknown_extension=True)
+
+    # override some config parameters
+    if config.model.HasField('ssd'):
+        config.model.ssd.feature_extractor.override_base_feature_extractor_hyperparams = True
+        if override_nms_score_threshold is not None:
+            config.model.ssd.post_processing.batch_non_max_suppression.score_threshold = override_nms_score_threshold
+        if override_resizer_shape is not None:
+            config.model.ssd.image_resizer.fixed_shape_resizer.height = override_resizer_shape[
+                0]
+            config.model.ssd.image_resizer.fixed_shape_resizer.width = override_resizer_shape[
+                1]
+    elif config.model.HasField('faster_rcnn'):
+        if override_nms_score_threshold is not None:
+            config.model.faster_rcnn.second_stage_post_processing.score_threshold = override_nms_score_threshold
+        if override_resizer_shape is not None:
+            config.model.faster_rcnn.image_resizer.fixed_shape_resizer.height = override_resizer_shape[
+                0]
+            config.model.faster_rcnn.image_resizer.fixed_shape_resizer.width = override_resizer_shape[
+                1]
+
+    tf_config = tf.ConfigProto()
+    tf_config.gpu_options.allow_growth = True
+
+    # export inference graph to file (initial), this will create tmp_dir
+    with tf.Session(config=tf_config):
+        with tf.Graph().as_default():
+            exporter.export_inference_graph(
+                INPUT_NAME,
+                config,
+                checkpoint_path,
+                tmp_dir,
+                input_shape=[max_batch_size, None, None, 3])
+
+    # read frozen graph from file
+    frozen_graph_path = os.path.join(tmp_dir, FROZEN_GRAPH_NAME)
+    frozen_graph = tf.GraphDef()
+    with open(frozen_graph_path, 'rb') as f:
+        frozen_graph.ParseFromString(f.read())
+
+    # apply graph modifications
+    if force_nms_cpu:
+        frozen_graph = f_force_nms_cpu(frozen_graph)
+    if replace_relu6:
+        frozen_graph = f_replace_relu6(frozen_graph)
+    if remove_assert:
+        frozen_graph = f_remove_assert(frozen_graph)
+
+    # get input names
+    output_names = [BOXES_NAME, CLASSES_NAME, SCORES_NAME, NUM_DETECTIONS_NAME]
+
+    # optionally perform TensorRT optimization
+    if use_trt:
+        with tf.Graph().as_default() as tf_graph:
+            with tf.Session(config=tf_config) as tf_sess:
+                frozen_graph = trt.create_inference_graph(
+                    input_graph_def=frozen_graph,
+                    outputs=output_names,
+                    max_batch_size=max_batch_size,
+                    max_workspace_size_bytes=max_workspace_size_bytes,
+                    precision_mode=precision_mode,
+                    minimum_segment_size=minimum_segment_size)
+
+                # perform calibration for int8 precision
+                if precision_mode == 'INT8':
+
+                    if calib_images_dir is None:
+                        raise ValueError('calib_images_dir must be provided for int8 optimization.')
+
+                    tf.import_graph_def(frozen_graph, name='')
+                    tf_input = tf_graph.get_tensor_by_name(INPUT_NAME + ':0')
+                    tf_boxes = tf_graph.get_tensor_by_name(BOXES_NAME + ':0')
+                    tf_classes = tf_graph.get_tensor_by_name(CLASSES_NAME + ':0')
+                    tf_scores = tf_graph.get_tensor_by_name(SCORES_NAME + ':0')
+                    tf_num_detections = tf_graph.get_tensor_by_name(
+                        NUM_DETECTIONS_NAME + ':0')
+                    
+                    image_paths = glob.glob(os.path.join(calib_images_dir, '*.jpg'))
+                    image_paths = image_paths[0:num_calib_images]
+
+                    for image_idx in tqdm.tqdm(range(0, len(image_paths), max_batch_size)):
+
+                        # read batch of images
+                        batch_images = []
+                        for image_path in image_paths[image_idx:image_idx+max_batch_size]:
+                            image = _read_image(image_path, calib_image_shape)           
+                            batch_images.append(image)
+
+                        # execute batch of images
+                        boxes, classes, scores, num_detections = tf_sess.run(
+                            [tf_boxes, tf_classes, tf_scores, tf_num_detections],
+                            feed_dict={tf_input: batch_images})
+
+                    pdb.set_trace()
+                    frozen_graph = trt.calib_graph_to_infer_graph(frozen_graph)
+
+    # re-enable variable batch size, this was forced to max
+    # batch size during export to enable TensorRT optimization
+    for node in frozen_graph.node:
+        if INPUT_NAME == node.name:
+            node.attr['shape'].shape.dim[0].size = -1
+
+    # write optimized model to disk
+    if output_path is not None:
+        with open(output_path, 'wb') as f:
+            f.write(frozen_graph.SerializeToString())
+
+    # remove temporary directory
+    subprocess.call(['rm', '-rf', tmp_dir])
+
+    return frozen_graph
+
+
+def download_dataset(dataset_name, output_dir='.'):
+    """Downloads a COCO dataset
+
+    Downloads a COCO dataset to the specified output directory.  A new
+    directory corresponding to the specified dataset will be created under
+    output_dir.  This directory will contain the images of the dataset.
+
+    Args
+    ----
+        dataset_name: A string representing the name of the dataset, it must
+            be one of the keys in trt_samples.object_detection.DATASETS.
+
+    Returns
+    -------
+        images_dir: A string representing the path of the directory containing
+            images of the dataset.
+        annotation_path: A string representing the path of the COCO annotation
+            file for the dataset.
+    """
+    global DATASETS
+
+    dataset = DATASETS[dataset_name]
+
+    subprocess.call(['mkdir', '-p', output_dir])
+
+    images_dir = os.path.join(output_dir, dataset.images_dir)
+    images_zip_file = os.path.join(output_dir,
+                                   os.path.basename(dataset.images_url))
+    annotation_path = os.path.join(output_dir, dataset.annotation_path)
+    annotation_zip_file = os.path.join(
+        output_dir, os.path.basename(dataset.annotation_url))
+
+    # download or use cached annotation
+    if os.path.exists(annotation_path):
+        print('Using cached annotation_path; %s' % (annotation_path))
+    else:
+        subprocess.call(
+            ['wget', dataset.annotation_url, '-O', annotation_zip_file])
+        subprocess.call(['unzip', annotation_zip_file, '-d', output_dir])
+
+    # download or use cached images
+    if os.path.exists(images_dir):
+        print('Using cached images_dir; %s' % (images_dir))
+    else:
+        subprocess.call(['wget', dataset.images_url, '-O', images_zip_file])
+        subprocess.call(['unzip', images_zip_file, '-d', output_dir])
+
+    return images_dir, annotation_path
+
+
+def benchmark_model(frozen_graph,
+                    images_dir,
+                    annotation_path,
+                    batch_size=1,
+                    image_shape=None,
+                    num_images=4096,
+                    tmp_dir='.benchmark_model_tmp_dir',
+                    remove_tmp_dir=True,
+                    output_path=None):
+    """Computes accuracy and performance statistics
+
+    Computes accuracy and performance statistics by executing over many images
+    from the MSCOCO dataset defined by images_dir and annotation_path.
+
+    Args
+    ----
+        frozen_graph: A GraphDef representing the object detection model to
+            test.  Alternatively, a string representing the path to the saved
+            frozen graph.
+        images_dir: A string representing the path of the COCO images
+            directory.
+        annotation_path: A string representing the path of the COCO annotation
+            file.
+        batch_size: An integer representing the batch size to use when feeding
+            images to the model.
+        image_shape: An optional tuple of integers representing a fixed shape
+            to resize all images before testing.
+        num_images: An integer representing the number of images in the
+            dataset to evaluate with.
+        tmp_dir: A string representing the path where the function may create
+            a temporary directory to store intermediate files.
+        output_path: An optional string representing a path to store the
+            statistics in JSON format.
+
+    Returns
+    -------
+        statistics: A named dictionary of accuracy and performance statistics
+        computed for the model.
+    """
+    if os.path.exists(tmp_dir):
+        if not remove_tmp_dir:
+            raise RuntimeError('Temporary directory exists; %s' % tmp_dir)
+        subprocess.call(['rm', '-rf', tmp_dir])
+    if batch_size > 1 and image_shape is None:
+        raise RuntimeError(
+            'Fixed image shape must be provided for batch size > 1')
+
+    from pycocotools.coco import COCO
+    from pycocotools.cocoeval import COCOeval
+
+    coco = COCO(annotation_file=annotation_path)
+
+    # get list of image ids to use for evaluation
+    image_ids = coco.getImgIds()
+    if num_images > len(image_ids):
+        print(
+            'Num images provided %d exceeds number in dataset %d, using %d images instead'
+            % (num_images, len(image_ids), len(image_ids)))
+        num_images = len(image_ids)
+    image_ids = image_ids[0:num_images]
+
+    # load frozen graph from file if string, otherwise must be GraphDef
+    if isinstance(frozen_graph, str):
+        frozen_graph_path = frozen_graph
+        frozen_graph = tf.GraphDef()
+        with open(frozen_graph_path, 'rb') as f:
+            frozen_graph.ParseFromString(f.read())
+    elif not isinstance(frozen_graph, tf.GraphDef):
+        raise TypeError('Expected frozen_graph to be GraphDef or str')
+
+    tf_config = tf.ConfigProto()
+    tf_config.gpu_options.allow_growth = True
+
+    coco_detections = []  # list of all bounding box detections in coco format
+    runtimes = []  # list of runtimes for each batch
+    image_counts = []  # list of number of images in each batch
+
+    with tf.Graph().as_default() as tf_graph:
+        with tf.Session(config=tf_config) as tf_sess:
+            tf.import_graph_def(frozen_graph, name='')
+            tf_input = tf_graph.get_tensor_by_name(INPUT_NAME + ':0')
+            tf_boxes = tf_graph.get_tensor_by_name(BOXES_NAME + ':0')
+            tf_classes = tf_graph.get_tensor_by_name(CLASSES_NAME + ':0')
+            tf_scores = tf_graph.get_tensor_by_name(SCORES_NAME + ':0')
+            tf_num_detections = tf_graph.get_tensor_by_name(
+                NUM_DETECTIONS_NAME + ':0')
+
+            # load batches from coco dataset
+            for image_idx in tqdm.tqdm(range(0, len(image_ids), batch_size)):
+                batch_image_ids = image_ids[image_idx:image_idx + batch_size]
+                batch_images = []
+                batch_coco_images = []
+
+                # read images from file
+                for image_id in batch_image_ids:
+                    coco_img = coco.imgs[image_id]
+                    batch_coco_images.append(coco_img)
+                    image_path = os.path.join(images_dir,
+                                              coco_img['file_name'])
+                    image = _read_image(image_path, image_shape)           
+                    batch_images.append(image)
+
+                # run once outside of timing to initialize
+                if image_idx == 0:
+                    boxes, classes, scores, num_detections = tf_sess.run(
+                        [tf_boxes, tf_classes, tf_scores, tf_num_detections],
+                        feed_dict={tf_input: batch_images})
+
+                # execute model and compute time difference
+                t0 = time.time()
+                boxes, classes, scores, num_detections = tf_sess.run(
+                    [tf_boxes, tf_classes, tf_scores, tf_num_detections],
+                    feed_dict={tf_input: batch_images})
+                t1 = time.time()
+
+                # log runtime and image count
+                runtimes.append(float(t1 - t0))
+                image_counts.append(len(batch_images))
+
+                # add coco detections for this batch to running list
+                batch_coco_detections = []
+                for i, image_id in enumerate(batch_image_ids):
+                    image_width = batch_coco_images[i]['width']
+                    image_height = batch_coco_images[i]['height']
+
+                    for j in range(int(num_detections[i])):
+                        bbox = boxes[i][j]
+                        bbox_coco_fmt = [
+                            bbox[1] * image_width,  # x0
+                            bbox[0] * image_height,  # x1
+                            (bbox[3] - bbox[1]) * image_width,  # width
+                            (bbox[2] - bbox[0]) * image_height,  # height
+                        ]
+
+                        coco_detection = {
+                            'image_id': image_id,
+                            'category_id': int(classes[i][j]),
+                            'bbox': bbox_coco_fmt,
+                            'score': float(scores[i][j])
+                        }
+
+                        coco_detections.append(coco_detection)
+
+    # write coco detections to file
+    subprocess.call(['mkdir', '-p', tmp_dir])
+    coco_detections_path = os.path.join(tmp_dir, 'coco_detections.json')
+    with open(coco_detections_path, 'w') as f:
+        json.dump(coco_detections, f)
+
+    # compute coco metrics
+    cocoDt = coco.loadRes(coco_detections_path)
+    eval = COCOeval(coco, cocoDt, 'bbox')
+    eval.params.imgIds = image_ids
+
+    eval.evaluate()
+    eval.accumulate()
+    eval.summarize()
+
+    statistics = {
+        'map': eval.stats[0],
+        'avg_latency_ms': 1000.0 * np.mean(runtimes),
+        'avg_throughput_fps': np.sum(image_counts) / np.sum(runtimes)
+    }
+
+    if output_path is not None:
+        subprocess.call(['mkdir', '-p', os.path.dirname(output_path)])
+        with open(output_path, 'w') as f:
+            json.dump(statistics, f)
+
+    subprocess.call(['rm', '-rf', tmp_dir])
+
+    return statistics
+
+
+def _read_image(image_path, image_shape):
+    image = Image.open(image_path).convert('RGB')
+    if image_shape is not None:
+        image = image.resize(image_shape[::-1])
+    return np.array(image)
diff --git a/tftrt/examples/object_detection/test.py b/tftrt/examples/object_detection/test.py
new file mode 100644
index 000000000..b7c2248c6
--- /dev/null
+++ b/tftrt/examples/object_detection/test.py
@@ -0,0 +1,101 @@
+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 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 argparse
+import json
+from .object_detection import download_model, download_dataset, optimize_model, benchmark_model
+
+
+def test(test_config_path):
+    """Runs an object detection test configuration
+    
+    This runs an object detection test configuration.  This involves
+    
+    1. Download a model architecture (or use cached).
+    2. Optimize the downloaded model architecrue
+    3. Benchmark the optimized model against a dataset
+    4. (optional) Run assertions to check the benchmark output
+
+    The input to this function is a JSON file which specifies the test
+    configuration.
+
+    example_test_config.json:
+
+        {
+            "source_model": { ... },
+            "optimization_config": { ... },
+            "benchmark_config": { ... },
+            "assertions": [ ... ]
+        }
+
+    source_model: A dictionary of arguments passed to download_model, which
+        specify the pre-optimized model architure.  The model downloaded (or
+        the cached model if found) will be passed to optimize_model.
+    optimization_config: A dictionary of arguments passed to optimize_model.
+        Please see help(optimize_model) for more details.
+    benchmark_config: A dictionary of arguments passed to benchmark_model.
+        Please see help(benchmark_model) for more details.
+    assertions: A list of strings containing python code that will be 
+        evaluated.  If the code returns false, an error will be thrown.  These
+        assertions can reference any variables local to this 'test' function.
+        Some useful values are
+
+            statistics['map']
+            statistics['avg_latency']
+            statistics['avg_throughput']
+
+    Args
+    ----
+        test_config_path: A string corresponding to the test configuration
+            JSON file.
+    """
+    with open(args.test_config_path, 'r') as f:
+        test_config = json.load(f)
+        print(json.dumps(test_config, sort_keys=True, indent=4))
+
+    # download model or use cached
+    config_path, checkpoint_path = download_model(**test_config['source_model'])
+
+    # optimize model using source model
+    frozen_graph = optimize_model(
+        config_path=config_path,
+        checkpoint_path=checkpoint_path,
+        **test_config['optimization_config'])
+
+    # benchmark optimized model
+    statistics = benchmark_model(
+        frozen_graph=frozen_graph,
+        **test_config['benchmark_config'])
+    print(json.dumps(statistics, sort_keys=True, indent=4))
+
+    # run assertions
+    if 'assertions' in test_config:
+        for a in test_config['assertions']:
+            if not eval(a):
+                raise AssertionError('ASSERTION FAILED: %s' % a)
+            else:
+                print('ASSERTION PASSED: %s' % a)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        'test_config_path',
+        help='Path of JSON file containing test configuration.  Please'
+             'see help(tftrt.examples.object_detection.test) for more information')
+    args=parser.parse_args()
+    test(args.test_config_path)
diff --git a/tftrt/examples/object_detection/third_party/cocoapi b/tftrt/examples/object_detection/third_party/cocoapi
new file mode 160000
index 000000000..ed842bffd
--- /dev/null
+++ b/tftrt/examples/object_detection/third_party/cocoapi
@@ -0,0 +1 @@
+Subproject commit ed842bffd41f6ff38707c4f0968d2cfd91088688
diff --git a/tftrt/examples/object_detection/third_party/models b/tftrt/examples/object_detection/third_party/models
new file mode 160000
index 000000000..402b561b0
--- /dev/null
+++ b/tftrt/examples/object_detection/third_party/models
@@ -0,0 +1 @@
+Subproject commit 402b561b03857151f684ee00b3d997e5e6be9778