Add dev roadmap

DEVELOPMENT.md

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+Development Roadmap
+==========
+
+IO module `deepposekit.io`
+------
+- [x] Abstract data IO with `deepposekit.io.BaseGenerator`
+- [x] Support for custom data sets by subclassing `deepposekit.io.BaseGenerator`
+- [x] Support for loading DeepLabCut formatted data `deepposekit.io.DLCDataGenerator`
+- [x] Utility function for initializing a new image set for annotation `deepposekit.io.utils.initialize_image_set`
+- [x] 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`
+- [ ] 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 functions for identifying outlier data for adding to data sets
+
+Models modules `deepposekit.models`
+------
+- [x] Add `MobileNetV2` and `DenseNet` backbones to `deepposekit.models.DeepLabCut`
+- [ ] Support arbitrary image sizes (not just powers of 2) with `tf.keras.layers.ZeroPaddding2D` 
+- [ ] Support dynamic image sizes with with automatic size 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 
+- [x] Add example notebook for using DeepLabCut formatted data
+- [ ] Add example for identifying outliers and appending new images to a training set
+
+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
+------
+- [x] Update to tf.keras (stand-alone keras will be deprecated)
+- [ ] Update to Tensorflow 2.0
+- [ ] `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`?