README.md

Cloud Annotations Training

Object detection walkthroughs

Training a model locally

Training a model on IBM Cloud

Classification walkthroughs

Training a model locally

Training a model on IBM Cloud

Quick & Dirty commands

It's recommended to go through one of the above walkthroughs, but if you already have and just need to remember one of the commands, here they are:

Project setup

git clone https://github.com/bourdakos1/tfjs-object-detection-training.git &&
cd tfjs-object-detection-training

• classification

  svn export -r 308 https://github.com/tensorflow/hub/trunk/examples/image_retraining classification
  echo > classification/__init__.py
  

• object detection

  svn export -r 8436 https://github.com/tensorflow/models/trunk/research/object_detection &&
  svn export -r 8436 https://github.com/tensorflow/models/trunk/research/slim
  

Training locally

python -m bucket.login

python -m bucket.download

• classification

  python -m classification.retrain \
    --image_dir=.tmp/data \
    --saved_model_dir=exported_graph/saved_model \
    --tfhub_module=https://tfhub.dev/google/imagenet/mobilenet_v1_100_224/feature_vector/1 \
    --how_many_training_steps=500 \
    --output_labels=.tmp/output_labels.txt
  

• object detection

  export PYTHONPATH=$PYTHONPATH:`pwd`/slim
  python -m object_detection.model_main \
    --pipeline_config_path=.tmp/pipeline.config \
    --model_dir=.tmp/checkpoint \
    --num_train_steps=500 &&
  python -m scripts.quick_export_graph
  

Training on IBM Cloud

python -m wml.login

• classification

  python setup_classification.py sdist
  

• object detection

  python setup_object_detection.py sdist
  

python -m wml.start_training_run

Convert to desired format

python -m scripts.convert --tfjs --tflite --coreml