Tutorial on how to create your own object detection dataset and train using TensorFlow's API
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README.md

TensorFlow Object Detection API Tutorial

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

Required Packages

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 
  1. Download and unzip this entire repo from GitHub, either interactively, or by entering
git clone https://github.com/wagonhelm/TF_ObjectDetection_API.git
  1. Install Python Requirement
cd TF_ObjectDetection_API
# Requires sudo if not in a virtual environment
pip3 install -r requirements.txt
pip3 install tensorflow jupyter
  1. 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 ..
  1. Launch Jupyter
jupyter notebook
  1. 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.