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Refactor constants management to use Pydantic BaseModel for configuration

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- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.
This commit is contained in:
Oleksandr Bezdieniezhnykh
2026-03-27 18:18:30 +02:00
parent b68c07b540
commit 142c6c4de8
106 changed files with 5706 additions and 654 deletions
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_docs/02_document/refactoring_notes.md
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1. update yolo to 26m version
2. don't use external augmentation, use built-in in yolo, put additional parameters for that in train command, each parameter should be on its own line with a proper comment
3. because of that, we don't need processed folder, just use data dir.
4. do not copy the files itself to dataset folder, use hard simlynks for that
5. unify constants directories in config - remove annotations-queue/config.yaml
and use constants for that