TensorFlow Object Detection API Tutorial

This repository has the code from my O'Reilly article published on October 25, 2017.

Required Packages

• TensorFlow v1.3
• Jupyter
• NumPy
• Scipy
• Matplotlib
• Scikit-Image
• Pandas
• lxml
• protobuf

There are two ways you can install these packages: by using Docker or by using native Python 3.5.

Using Docker

1. Download and install Docker. If using Ubuntu 14.04/16.04 I wrote my own instructions for installing docker here.

2. Download and unzip this entire repo from GitHub, either interactively, or by entering

   git clone https://github.com/wagonhelm/TF_ObjectDetection_API.git
   

3. Open your terminal and use cd to navigate into the directory of the repo on your machine

   cd TF_ObjectDetection_API

4. To build the Dockerfile, enter

   docker build -t object_dockerfile -f dockerfile .

   If you get a permissions error on running this command, you may need to run it with sudo:

   sudo docker build -t object_dockerfile -f dockerfile .

5. Run Docker from the Dockerfile you've just built

   docker run -it -p 8888:8888 -p 6006:6006 object_dockerfile bash

   or

   sudo docker run -it -p 8888:8888 -p 6006:6006 object_dockerfile bash

   if you run into permission problems.

6. Install TensorFlow Object Detection API

   cd models/research/
   protoc object_detection/protos/*.proto --python_out=.
   export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
   cd ..
   cd ..

7. Launch Jupyter and Tensorboard both by using tmux

   tmux
   
   jupyter notebook --allow-root

   Press CTL+B then C to open a new tmux window, then

   tensorboard --logdir='data'

   To switch windows Press CTL+B then window #

   Once both jupyter and tensorboard are running, using your browser, navigate to the URLs shown in the terminal output if those don't work try http://localhost:8888/ for Jupyter Notebook and http://localhost:6006/ for Tensorboard. I had issues with using TensorBoard with Firefox when launched from Docker.

Using Native Python 3

1. Install system requirements

sudo apt-get install -y git-core wget protobuf-compiler 

2. Download and unzip this entire repo from GitHub, either interactively, or by entering

git clone https://github.com/wagonhelm/TF_ObjectDetection_API.git

3. Install Python Requirement

cd TF_ObjectDetection_API
# Requires sudo if not in a virtual environment
pip3 install -r requirements.txt
pip3 install tensorflow jupyter

4. Clone TensorFlow Models Into Repository Directory and Install Object Detection API

cd TF_ObjectDetection_API
git clone https://github.com/tensorflow/models.git

You will have to run this command every time you close your terminal unless you add the the path to slim to your .bashrc file

cd models/research/
protoc object_detection/protos/*.proto --python_out=.
export PYTHONPATH=$PYTHONPATH:`pwd`:`pwd`/slim
cd ..
cd ..

4. Launch Jupyter

jupyter notebook

5. Launch Tensorboard In New Terminal

tensorboard --logdir='data'

Once both jupyter and tensorboard are running, using your browser, navigate to the URLs shown in the terminal output if those don't work try http://localhost:8888/ for Jupyter Notebook and http://localhost:6006/ for Tensorboard.