Safety vulnerability ID: 38133
The information on this page was manually curated by our Cybersecurity Intelligence Team.
Deeposlandia 0.6 updates its dependency 'Tensorflow' to v1.15 to include security fixes.
Latest version: 0.8.0
Automatic detection and semantic image segmentation with deep learning
*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).
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