/training

🐝 Custom Object Detection and Classification Training

Primary LanguagePythonMIT LicenseMIT

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