packaging: add a change log file

CHANGELOG.md

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+# Changelog
+
+Pour suivre l'évolution des différentes versions.
+Le format de ce fichier est basé sur Keep a Changelog,
+et ce projet respecte le Semantic Versioning.
+Les sections conserveront leur nom en anglais.
+
+## Unreleased
+
+### Added
+
+### Changed
+
+### Removed
+
+## v0.6 (2020-04-01)
+
+*Georeferenced dataset post-processing*
+
+This release essentially copes with the georeferenced dataset, one may now post-process
+the results, so as to visualize labelled masks as raster. A vectorized version of each
+prediction is also available.
+
+As another major evolution, `deeposlandia` now has a Command-Line Interface (CLI). The
+available commands are `datagen`, `train`, `infer` and `postprocess` respectively for
+generating preprocessed datasets, training neural networks, doing inference and
+post-processing neural network outputs.
+
+### Added
+
+- Set up a Command-Line Interface (#90).
+- Consider `RGBA` images and warns the user as this format is not handled by the web app
+  (#107).
+- Consider geometric treatments in a dedicated module, add vector-to-raster and
+  raster-to-vector transformation steps ; save postprocessed images as vector and raster
+  files (#119).
+- Postprocess aerial images so as to produce predicted rasters (#118, #126, #127).
+- Add missing test files for Tanzania dataset.
+- Some information about GDPR in the web app (#113).
+- Improve unit tests dedicated to georeferenced data processing (#104).
+
+### Changed
+
+- Label folders are standardized (`labels`), in particular this folder name replaces `gt`
+  for `Aerial` dataset (#139).
+- Always use the best existing model, instead of parametrizing the access to the model
+  (#135).
+- Broken images are considered, hence not serialized onto the file system (#129).
+- The georeferenced aerial datasets are updated and factorized into a generic
+  `GeoreferencedDataset` class, the test files are updated accordingly (#128).
+- Deep learning model are now known as `featdet` and `semseg` instead of
+  `feature_detection` and `semantic_segmentation` (#133).
+- Update the training metric history when using a existing trained model (#102).
+- Move the documentation to a dedicated folder.
+- Some code cleaning operations, using `black` and `flake8` (#120).
+- Update dependencies, especially `Tensorflow`, due to vulnerability issues.
+- Fix the unit tests for Tanzania dataset population (#111).
+- The process quantity is an argument of `populate()` functions, in order to implement
+  multiprocessing (#110).
+- Logger syntax has been refactored (%-format) (#103).
+
+### Removed
+
+- The concept of "agregated dataset" is removed, as we consider a home-made Mapillary
+  dataset version. As a consequence, some input/output folder paths have been updated
+  (#134).
+- The hyperparameter optimization script (`paramoptim.py`) has been removed, `train.py`
+  can handle several value for each parameter (#125).
+
+## v0.5 (2019-01-24)
+
+*Georeferenced datasets and web application*
+
+Some new datasets focusing on building footprint detection have been introduced in the
+framework, namely Inria Aerial Image dataset and Open AI Tanzania dataset.
+
+Some new state-of-the-art deep neural network architectures have been implemented to
+enrich the existing collection, and design more sophisticated models.
+
+Furthermore a bunch of Jupyter notebooks has been written to make the framework usage
+easier, and clarify deep learning pipelines, from dataset description to model training
+and inference.
+
+And last but not least, a light Flask Web application has been developed to showcase some
+deep learning predictions. Oslandia hosts this Web app at
+http://data.oslandia.io/deeposlandia.
+
+## v0.4 (2018-05-03)
+
+*Train convolutional neural networks with Keras API*
+
+This new release is characterized by the transition from the TensorFlow library to the
+Keras library so as to train neural networks and predict image labels.
+
+Additionally, the code has been structured in a production-like purpose:
+
+- the program modules have been moved to a deeposlandia repository;
+- a tests repository contains a bunch of tests that guarantee the code validity;
+- a setup.py file summarizes the project description and target. Some complements may
+  arise in order to publish the project on Pypi.
+
+## v0.3.2 (2018-03-28)
+
+*Validate and test the trained a wider range of TensorFlow models*
+
+In this patch, a more mature code is provided:
+
+- Dataset handling is factorized, we can now consider Mapillary or shape datasets
+  starting from a common Dataset basis
+- Model handling is factorized, we can generate feature detection models or semantic
+  segmentation models, with common behaviors (basic layer creation, for instance)
+- Some state-of-the-art models have been implemented (VGG, Inception)
+- A base of code has been deployed for considering Keras API (the switch from TensorFlow
+  to Keras will be the object of a next minor release)
+
+## v0.3.1 (2018-03-13)
+
+*Validate and test the trained model (Minor README fixes)*
+
+Fix the 0.3 release with minor changes around README.md file (picture updates,
+essentially).
+
+## v0.3 (2018-03-13)
+
+*Validate and test the trained model*
+
+- Add a single-batched validation phase during training process, the corresponding
+  metrics are logged onto Tensorboard so as to be compared with training metrics (same
+  graphs) ;
+- Add a model inference module, that call the test() method of
+  ConvolutionalNeuralNetwork: it takes a trained model as an input, and infer label
+  occurrences on a image testing set ;
+- Manage the Tensorboard monitoring in a more clever way ;
+- Add the possibility to gather similar labels for Mapillary dataset: by aggregating
+  them, the number of labels decreases and the model may become easier to
+  train. :warning: With this new feature, the dataset structure in json files has been
+  modified: the labels keys are now dictionaries (instead of a lists) that link class ids
+  (keys) and label occurrences (values), for each image.
+
+## v0.2 (2018-01-17)
+
+*Object-oriented convolutional neural network*
+
+This new release provide an improved version of the project by considering
+object-oriented programming.
+
+- The project is structured around Dataset and ConvolutionalNeuralNetwork classes. These
+  classes are written in dedicated modules.
+- As a consequence, the main module contains only program-specific code (argument
+  handling).
+- A second dataset has been introduced to the project (geometric shapes), so as to make
+  development easier and more reliable.
+
+## v0.1 (2017-12-19)
+
+*Street-scene object detection*
+
+This repository runs a convolutional neural network on Mapillary Vistas Dataset, so as to
+detect a range of street-scene objects (car, pedestrian, street-lights, pole,
+... ). Developments are still under progress, as the model is unable to provide a
+satisfying detection yet.