updated Readme to match latest status

Author: sjentzsch

README.md

@@ -1,21 +1,21 @@
 # tf_vision
 
-On 15th June 2017 Google released their (pretrained) Computer Vision models via the TensorFlow Object Detection API.  
+On 15th June 2017 Google released their first (pretrained) Computer Vision models via the TensorFlow Object Detection API.  
 These models won the COCO detection challenge (http://cocodataset.org/) in 2016. COCO features a large-scale dataset with more than 200k labeled images, 80 object categories and 250k people with keypoints.  
 
-This repository derives from the Jupyter notebook of TensorFlow's Object Detection API and includes an Ansible playbook to easily setup your system to run (pretrained) models, to do fancy things incl. infering on live video data. Applying it to Robotics is one of the major goals.
+This repository builds upon the Jupyter notebook of TensorFlow's Object Detection API, includes an Ansible playbook to automatically setup your system to run (pretrained) models, allowing for interesting use cases when it comes to infering on video data. Applying it to Robotics is one of the major goals, though.
 
-Find here some highly recommended links:
-* Google announcement: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
+Firstly, find here some highly recommended links:
+* Google initial announcement: https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html
 * COCO detection challenge (incl. dataset, results): http://cocodataset.org/
 * CVPR 2017 Paper describing the models in detail: https://arxiv.org/abs/1611.10012
 * TensorFlow research model and object_detection API: https://github.com/tensorflow/models/tree/master/research/object_detection
 
-This repository assumes you have an Ubuntu 16.04 x86_64 running Python 3.(5.X).
+This repository assumes you have an Ubuntu 16.04 x86_64. You can choose between using either Python 2.7 or 3.5.
 
-# GPU Acceleration
+# Requirements for GPU Acceleration
 
-> Note: As of current TensorFlow version 1.4.0: `All our prebuilt binaries have been built with CUDA 8 and cuDNN 6. We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.` However, building from source with 1.4.0 using CUDA 9 and cuDNN 7 should be fine and working.
+> Note: As of current TensorFlow version 1.4.0: `All our prebuilt binaries have been built with CUDA 8 and cuDNN 6. We anticipate releasing TensorFlow 1.5 with CUDA 9 and cuDNN 7.` However, building from source with 1.4.0 using CUDA 9 and cuDNN 7 is working well already.
 
 For GPU-accelerated tensorflow, ensure you do or have done the following: 
 
@@ -38,21 +38,21 @@ For GPU-accelerated tensorflow, ensure you do or have done the following:
 
    Then switch to respective latest stable version branch (System Settings -> Software & Updates -> Additional Drivers).
 
-3. Install latest CUDA:
+3. Install CUDA:
 
-   Download the latest NVIDIA CUDA Toolkit at https://developer.nvidia.com/cuda-downloads (select `deb (local)` in the end), then follow the Installation Instructions below.
+   Download the respective NVIDIA CUDA Toolkit at https://developer.nvidia.com/cuda-downloads (select `deb (local)` in the end), then follow the Installation Instructions below.
 
    For further notes, refer to: http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
 
    To uninstall previous versions please refer to: http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#handle-uninstallation
 
    Note: Our Ansible provisioning will take care of setting PATH and LD_LIBRARY_PATH environment variables properly.
 
-3. Install latest NVIDIA CUDA Deep Neural Network library (cuDNN)
+3. Install NVIDIA CUDA Deep Neural Network library (cuDNN)
 
    First, you need to register for the NVIDIA Developer Program at https://developer.nvidia.com/accelerated-computing-developer
 
-   Then, download latest version at https://developer.nvidia.com/rdp/cudnn-download of the following Debian Files:
+   Then, download respective version at https://developer.nvidia.com/rdp/cudnn-download of the following Debian Files (e.g.):
 
   * cuDNN v7.0 Runtime Library for Ubuntu16.04 (Deb)
   * cuDNN v7.0 Developer Library for Ubuntu16.04 (Deb)
@@ -72,31 +72,34 @@ For GPU-accelerated tensorflow, ensure you do or have done the following:
 
 1. Setup log folder for Ansible (replace `sjentzsch` with your local user to run Ansible from):
 
-```
-sudo mkdir -p /var/log/ansible/ && sudo chown -R sjentzsch:adm /var/log/ansible/ && sudo chmod 2750 /var/log/ansible/
-```
+   ```
+   sudo mkdir -p /var/log/ansible/ && sudo chown -R sjentzsch:adm /var/log/ansible/ && sudo chmod 2750 /var/log/ansible/
+   ```
 
-2. Create a new and clean folder called e.g. `ml` somewhere, and inside, checkout `setup_common_lib` and this GitHub repository (here with SSH keys):
+2. Create a new and clean folder called e.g. `ml` somewhere, and inside, clone `setup_common_lib` and this GitHub repository (here with SSH keys):
 
-```
-git clone ssh://git@scr.bsh-sdd.com:7999/civtm/setup_common_lib.git
-git clone git@github.com:sjentzsch/tf_vision.git
-```
+   ```
+   git clone ssh://git@scr.bsh-sdd.com:7999/civtm/setup_common_lib.git
+   git clone git@github.com:sjentzsch/tf_vision.git
+   ```
 
-Note: Currently, the shared library `setup_common_lib.git` lives inside BSH SDD.
+   Note: Currently, the repository `setup_common_lib` lives inside restricted BSH SDD.
 
-3. Inside `ml/tf_vision/ansible/` setup your computer by running Ansible via:
+3. Inside `ml/tf_vision/ansible/` setup your machine by running Ansible:
 
-```
-ansible-playbook -i hosts -v local.yml -K
-```
+   ```
+   ansible-playbook -i hosts -v local.yml -K
+   ```
 
-Ansible will immediately ask you for your sudo password, as packages etc. will need to be installed through root user.
+   Ansible will immediately ask you for your sudo password, as packages etc. will need to be installed through root user.
 
 # Run
 
-Inside `ml/tf_vision/` run:
+Inside `ml/tf_vision/` simply run:
 
 ```
-python3 object_detection_tutorial.py
+# Python 2.7
+python obj_detect.py
+# Python 3.5
+python3 obj_detect.py
 ```