Hands Detection

Hands video tracker using the Tensorflow Object Detection API and Faster RCNN model. The data used is the "Hand Dataset" from University of Oxford. The dataset can be found here. More informations: "Hand detection using multiple proposals", A. Mittal, A. Zisserman, P. H. S. Torr, British Machine Vision Conference, 2011.

You can find demo here.

Installation

First we need to install the Tensorflow Object Detection API. You can either install dependencies or run the provided docker image.

Installing dependencies

Please follow Tensorflow Object Detection API installation tutorial in models/ directory

Using docker

We use the gcr.io/tensorflow/tensorflow image, so we have already Jupyter and Tensorboard services. The TOD API is already installed, the next step is to pull data from Hands Dataset.

docker build -t hands-tracker .
docker run -it -p 8888:8888 -p 6006:6006 hands-tracker bash

Training on Google Cloud ML

Pulling data from the Oxford University Hands Dataset

To pull data in dataset/ directory, use the following python script:

python create_inputs_from_dataset.py

If you need more informations about pulling data from University of Oxford, or MAT files to TFRecord files conversion, see the IPython notebook for generating inputs The dataset folder should be structured as following:

dataset/
|---  test_dataset/
|------  test_data/
|----------  images/
|----------  annotations/
|---  training_dataset/
|------  training_data/
|----------  images/
|----------  annotations/
|---  validation_dataset/
...

Deploy model to Google Cloud Storage

To make following steps easier, use the following variables:

export GC_PROJECT_ID=<your_project_id>
export GCS_BUCKET=<your_gcs_bucket>

First, we have to log in with our Google Cloud account and setup config

gcloud auth login
gcloud config set project $GC_PROJECT_ID
gcloud auth application-default login

Next, we can deploy our project files to Google Cloud Storage. You can use the following script:

./deploy_on_gcs.sh $GCS_BUCKET

Create training and eval jobs

Our project is ready for training. We can create our job on Google Cloud ML

gcloud ml-engine jobs submit training `whoami`_object_detection_`date +%s` \
    --job-dir=gs://${GCS_BUCKET}/train \
    --packages models/dist/object_detection-0.1.tar.gz,models/slim/dist/slim-0.1.tar.gz \
    --module-name object_detection.train \
    --region us-central1 \
    --scale-tier BASIC \
    -- \
    --train_dir=gs://${GCS_BUCKET}/train \
    --pipeline_config_path=gs://${GCS_BUCKET}/data/faster_rcnn_resnet101_hands.config

The scale tier used here is 'BASIC' and training takes absolutely forever, but with 'BASIC_GPU' config training takes approximatively two hours. Be aware that after job began you'll be charged on your credit card.

Once the job has started, you can run an evaluation job as following:

gcloud ml-engine jobs submit training `whoami`_object_detection_eval_`date +%s` \
    --job-dir=gs://${GCS_BUCKET}/train \
    --packages models/dist/object_detection-0.1.tar.gz,models/slim/dist/slim-0.1.tar.gz \
    --module-name object_detection.eval \
    --region us-central1 \
    --scale-tier BASIC_GPU \
    -- \
    --checkpoint_dir=gs://${GCS_BUCKET}/train \
    --eval_dir=gs://${GCS_BUCKET}/eval \
    --pipeline_config_path=gs://${GCS_BUCKET}/data/faster_rcnn_resnet101_hands.config

Monitoring

Finally, if you are using the provided docker image, you can monitor your training job with Tensorboard:

tensorboard --logdir=gs://${GCS_BUCKET}