🐝 Custom Object Detection and Classification Training
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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/cloud-annotations/training.git &&
cd 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
    mkdir exported_graph
    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=exported_graph/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