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Development Roadmap

IO module deepposekit.io

  • Abstract data IO with deepposekit.io.BaseGenerator
  • Support for custom data sets by subclassing deepposekit.io.BaseGenerator
  • Support for loading DeepLabCut formatted data deepposekit.io.DLCDataGenerator
  • Utility function for initializing a new image set for annotation deepposekit.io.utils.initialize_image_set
  • Utility function for merging a new image set to an existing dataset deepposekit.io.utils.merge_new_images
  • Add methods for appending new images to deepposekit.io.BaseGenerator with deepposekit.io.BaseGenerator.append_images()
  • Utility function for merging multiple arbitrary deepposekit.io.BaseGenerator with deepposekit.io.utils.merge_data
  • Utility function for converting deepposekit.io.DLCDataGenerator data to deepposekit.io.DataGenerator data and vice-versa
  • Support more DLC features within deepposekit.io.DLCDataGenerator.
  • Support passing multiple deepposekit.io.BaseGenerator for deepposekit.io.TrainingGenerator, but ensure all are compatible before training the model.

Annotation module deepposekit.annotate

  • Add support for deepposekit.annotate.Annotator to edit DeepLabCut formatted data deepposekit.io.DLCDataGenerator. Ensure this does not destroy compatibility with DLC.
  • Remove extra step of initializing a skeleton and remove deepposekit.annotate.Skeleton, as this is confusing and not all that helpful.
  • Abstract deepposekit.annotate.gui.GUI and deepposekit.annotate.Annotator to use new deepposekit.io.BaseGenerator with abstracted data IO
  • Develop submodule deepposekit.annotate.outliers with tools for identifying outlier data for adding to data sets

Models modules deepposekit.models

  • Add MobileNetV2 and DenseNet backbones to deepposekit.models.DeepLabCut
  • Add pretrained DenseNet frontend to StackedDenseNet model
  • Support arbitrary image sizes (not just powers of 2) with tf.keras.layers.ZeroPaddding2D
  • Support dynamic image sizes with with automatic padding at inference. Is this possible without reducing functionality?

Examples and Documentation

  • Improve and update docstrings across the package
  • Add example notebook for using custom data sets
  • Add example notebook for using DeepLabCut formatted data
  • Add example for identifying outliers and appending new images to a training set
  • Add html documentation

Tests (once API has stabilized)

  • Import all modules and submodules
  • Download example data
  • Run training for all models
  • Save model
  • Load model
  • Resume training
  • Predict on new data

Future

  • Put deepposekit on PyPI
  • Update to tf.keras (stand-alone keras will be deprecated)
  • Update to Tensorflow 2.0
  • deepposekit.visualize module with functions for making videos and plotting data
  • deepposekit.pose3d module? Does it make sense to support this, or just make the API abstract enough to let others use their own solution for 3D?
  • deepposekit.localize module. Train models that localize individuals using confidence maps. Update and further abstract deepposekit.annotate, deepposekit.models, etc.
  • deepposekit.multiple module. Add support for small groups of multiple individuals? Does it make sense to support this or focus on deepposekit.localize?