Added table of models releases

Author: EdjeElectronics

README.md

@@ -58,9 +58,22 @@ Create a folder directly in C: and name it “tensorflow1”. This working direc
 
 Download the full TensorFlow object detection repository located at https://github.com/tensorflow/models by clicking the “Clone or Download” button and downloading the zip file. Open the downloaded zip file and extract the “models-master” folder directly into the C:\tensorflow1 directory you just created. Rename “models-master” to just “models”.
 
-(Note: The TensorFlow models repository's code (which contains the object detection API) is continuously updated by the developers. Sometimes they make changes that break functionality with old versions of TensorFlow. It is always best to use the latest version of TensorFlow and download the latest models repository. 
+**Note: The TensorFlow models repository's code (which contains the object detection API) is continuously updated by the developers. Sometimes they make changes that break functionality with old versions of TensorFlow. It is always best to use the latest version of TensorFlow and download the latest models repository. If you are not using the latest version, clone or download the commit for the version you are using as listed in the table below.**
 
-This tutorial was originally done using TensorFlow v1.5 and this [GitHub commit](https://github.com/tensorflow/models/tree/079d67d9a0b3407e8d074a200780f3835413ef99) of the TensorFlow Object Detection API. If portions of this tutorial do not work, it may be necessary to install TensorFlow v1.5 and use this exact commit rather than the most up-to-date version.)
+ If you are using an older version of TensorFlow, here is a table showing which GitHub commit of the repository you should use. I generated this by going to the release branches for the models repository and getting the commit before the last commit for the branch. (They remove the research folder as the last commit before they create the official version release.)
+
+| TensorFlow version | GitHub Models Repository Commit |
+|--------------------|---------------------------------|
+|TF v1.7             |https://github.com/tensorflow/models/tree/adfd5a3aca41638aa9fb297c5095f33d64446d8f |
+|TF v1.8             |https://github.com/tensorflow/models/tree/abd504235f3c2eed891571d62f0a424e54a2dabc |
+|TF v1.9             |https://github.com/tensorflow/models/tree/d530ac540b0103caa194b4824af353f1b073553b |
+|TF v1.10            |https://github.com/tensorflow/models/tree/b07b494e3514553633b132178b4c448f994d59df |
+|TF v1.11            |https://github.com/tensorflow/models/tree/23b5b4227dfa1b23d7c21f0dfaf0951b16671f43 |
+|TF v1.12            |https://github.com/tensorflow/models/tree/r1.12.0 |
+|TF v1.13            |https://github.com/tensorflow/models/tree/r1.13.0 |
+|Latest version      |https://github.com/tensorflow/models |
+
+This tutorial was originally done using TensorFlow v1.5 and this [GitHub commit](https://github.com/tensorflow/models/tree/079d67d9a0b3407e8d074a200780f3835413ef99) of the TensorFlow Object Detection API. If portions of this tutorial do not work, it may be necessary to install TensorFlow v1.5 and use this exact commit rather than the most up-to-date version.
 
 #### 2b. Download the Faster-RCNN-Inception-V2-COCO model from TensorFlow's model zoo
 TensorFlow provides several object detection models (pre-trained classifiers with specific neural network architectures) in its [model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md). Some models (such as the SSD-MobileNet model) have an architecture that allows for faster detection but with less accuracy, while some models (such as the Faster-RCNN model) give slower detection but with more accuracy. I initially started with the SSD-MobileNet-V1 model, but it didn’t do a very good job identifying the cards in my images. I re-trained my detector on the Faster-RCNN-Inception-V2 model, and the detection worked considerably better, but with a noticeably slower speed.
@@ -391,7 +404,7 @@ If you encounter errors, please check out the Appendix: it has a list of errors
 ## Appendix: Common Errors
 It appears that the TensorFlow Object Detection API was developed on a Linux-based operating system, and most of the directions given by the documentation are for a Linux OS. Trying to get a Linux-developed software library to work on Windows can be challenging. There are many little snags that I ran in to while trying to set up tensorflow-gpu to train an object detection classifier on Windows 10. This Appendix is a list of errors I ran in to, and their resolutions.
 
-#### 1. ModuleNotFoundError: No module named 'deployment'
+#### 1. ModuleNotFoundError: No module named 'deployment' or No module named 'nets'
 
 This error occurs when you try to run object_detection_tutorial.ipynb or train.py and you don’t have the PATH and PYTHONPATH environment variables set up correctly. Exit the virtual environment by closing and re-opening the Anaconda Prompt window. Then, issue “activate tensorflow1” to re-enter the environment, and then issue the commands given in Step 2e.