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

• 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.

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.

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.

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'