Update README.md

README.md

@@ -11,6 +11,8 @@ gains on the Jetson TX2 as seen [below](#models).
 
 * [Model overview](#models)
 
+* [Setup](#install)
+
 * [Download models and create frozen graphs](#download)
 
 * [Convert frozen graph to TensorRT engine](#convert)
@@ -51,6 +53,42 @@ data transfer back from GPU.  Time does not include preprocessing.
 See [scripts/test_tf.py](scripts/test_tf.py), [scripts/test_trt.py](scripts/test_trt.py), and [src/test/test_trt.cu](src/test/test_trt.cu) 
 for implementation details. 
 
+<a name="install"></a>
+## Setup
+
+1. Flash the Jetson TX2 using JetPack 3.2.  Be sure to install
+   * CUDA 9.0
+   * OpenCV4Tegra
+   * cuDNN
+   * TensorRT 3.0
+
+2. Install TensorFlow on Jetson TX2.
+   1. Download TensorFlow 1.5.0 pip wheel from [here](https://drive.google.com/open?id=1BNOaSdfd6YyitTa4DLD7j4L45-Duo2LR).
+   2. Install TensorFlow using pip
+  
+            sudo pip install tensorflow-1.5.0rc0-cp27-cp27mu-linux_aarch64.whl
+
+3. Install uff exporter on Jetson TX2.
+   1. Download TensorRT 3.0.4 for Ubuntu 16.04 and CUDA 9.0 tar package from https://developer.nvidia.com/nvidia-tensorrt-download.
+   2. Extract archive 
+
+            tar -xzf TensorRT-3.0.4.Ubuntu-16.04.3.x86_64.cuda-9.0.cudnn7.0.tar.gz
+
+   3. Install uff python package using pip 
+
+            sudo pip install TensorRT-3.0.4/uff/uff-0.2.0-py2.py3-none-any.whl
+
+4. Clone and build this project
+
+    ```
+    git clone --recursive https://github.com/NVIDIA-Jetson/tf_to_trt_image_classification.git
+    cd tf_to_trt_image_classification
+    mkdir build
+    cd build
+    cmake ..
+    make 
+    cd ..
+    ```
 
 <a name="download"></a>
 ## Download models and create frozen graphs