/TF_ObjectDetection_API

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

Primary LanguageJupyter Notebook

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.

System information

What is the top-level directory of the model you are using: research/ Have I written custom code (as opposed to using a stock example script provided in TensorFlow): Yes OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Linux Ubuntu 16.04 TensorFlow installed from (source or binary): No CUDA/cuDNN version: 8.0/6.0 GPU model and memory: 1080 ti Exact command to reproduce: python -m object_detection/metrics/offline_eval_map_corloc --eval_dir=PATH/TO/EVAL_DIR --eval_config_path=PATH/TO/EVAL_CONGIF.pbtxt --input_config_path=PATH/TO/INPUT_CONFIG.pbtxt

You can obtain the TensorFlow version with

python -c "import tensorflow as tf; print(tf.GIT_VERSION, tf.VERSION)" ('v1.4.0-rc1-11-g130a514', '1.4.0')

Describe the problem

object_detection_evaluation states that having the field standard_fields.InputDataFields.groundtruth_difficult is optional. However, it checks whether the field exists or not like this: groundtruth_dict[standard_fields.InputDataFields.groundtruth_difficult].size For me, I just removed the .size part and the error got away but you may want to do it in a better way.

INFO:tensorflow:Processing file: /PATH/TO/test_detections.tfrecord-00000-of-00001 INFO:tensorflow:Processed 0 images... Traceback (most recent call last): File "/anaconda2/lib/python2.7/runpy.py", line 174, in _run_module_as_main "main", fname, loader, pkg_name) File "/anaconda2/lib/python2.7/runpy.py", line 72, in _run_code exec code in run_globals File "models/research/object_detection/metrics/offline_eval_map_corloc.py", line 173, in tf.app.run(main) File "/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "models/research/object_detection/metrics/offline_eval_map_corloc.py", line 166, in main metrics = read_data_and_evaluate(input_config, eval_config) File "models/research/object_detection/metrics/offline_eval_map_corloc.py", line 124, in read_data_and_evaluate decoded_dict) File "object_detection/utils/object_detection_evaluation.py", line 174, in add_single_ground_truth_image_info (groundtruth_dict[standard_fields.InputDataFields.groundtruth_difficult] AttributeError: 'NoneType' object has no attribute 'size'