Update and rename README.md to TUTORIAL.md

Author: EdjeElectronics

README.md renamed to TUTORIAL.md

@@ -64,7 +64,7 @@ You can choose which model to train your objection detection classifier on. If y
 
 This tutorial will use the Faster-RCNN-Inception-V2 model. [Download the model here](http://download.tensorflow.org/models/object_detection/faster_rcnn_inception_v2_coco_2018_01_28.tar.gz) Open the downloaded faster_rcnn_inception_v2_coco_2018_01_28.tar.gz file with a file archiver such as WinZip or 7-Zip and extract the faster_rcnn_inception_v2_coco_2018_01_28 folder to the C:\tensorflow1\models\research\object_detection folder. (Note: The model date and version will likely change in the future, but it should still work with this tutorial.)
 
-#### 2c. Download this tutorial's repository from GitHub
+#### 2c. Download this tutorial's repository from GitHub, and inference graph from Dropbox
 Download the full repository located on this page (scroll to the top and click Clone or Download) and extract all the contents directly into the C:\tensorflow1\models\research\object_detection directory. This establishes a specific directory structure that will be used for the rest of the tutorial. 
 
 At this point, here is what your \object_detection folder should look like:
@@ -75,13 +75,14 @@ At this point, here is what your \object_detection folder should look like:
 
 This repository contains the images, annotation data, .csv files, and TFRecords needed to train a "Pinochle Deck" playing card detector. You can use these images and data to practice making your own Pinochle Card Detector. It also contains Python scripts that are used to generate the training data, as well as test to test out the object detection classifier on images, videos, or a webcam feed. You can ignore the \doc folder and its files, they are just there to hold the images used for this readme.
 
-If you want to practice training your own "Pinochle Deck" card detector, you can leave all the files as they are. You can follow along with this tutorial to see how each of the files were generated, and then run the training.
+If you want to practice training your own "Pinochle Deck" card detector, you can leave all the files as they are. You can follow along with this tutorial to see how each of the files were generated, and then run the training. You will still need to generate the TFRecord files (train.record and test.record) as described in Step 4. 
+
+You can also download the frozen inference graph for my trained Pinochle Deck card detector [from this Dropbox link](https://www.dropbox.com/s/va9ob6wcucusse1/inference_graph.zip?dl=0) and extract the contents to \object_detection\inference_graph. This inference graph will work "out of the box", and you can test after all the setup instructions in Step 2a - 2f have been completed by running the Object_detection_image.py (or video or webcam) script.
 
 If you want to train your own object detector, delete the following files (do not delete the folders):
 - All files in \object_detection\images\train and \object_detection\images\test
 - The “test_labels.csv” and “train_labels.csv” files in \object_detection\images
 - All files in \object_detection\training
-- The “train.record” and “test.record” files in \object_detection
 -	All files in \object_detection\inference_graph
 
 Now, you are ready to start from scratch in training your own object detector. This tutorial will assume that all the files listed above were deleted, and will go on to explain how to generate the files for your own training dataset.
@@ -117,7 +118,7 @@ Install the other necessary packages by issuing the following commands:
 The PATH variable must be configured to add the \models, \models\research, and \models\research\slim directories. As an electrical engineer, I'm not really sure how environment variables work, but for some reason, you need to create a PYTHONPATH variable with these directories, and then add PYTHONPATH to the PATH variable. Otherwise, it will not work. Do this by issuing the following commands (from any directory):
 ```
 (tensorflow1) C:\> set PYTHONPATH=C:\tensorflow1\models;C:\tensorflow1\models\research;C:\tensorflow1\models\research\slim
-(tensorflow1) C:\> set PATH=%PATH%;%PYTHONPATH%
+(tensorflow1) C:\> set PATH=%PATH%;PYTHONPATH
 ```
 *Note: Every time the "tensorflow1" virtual environment is exited, the PATH and PYTHONPATH variables are reset and need to be set up again.*