Refactor constants management to use Pydantic BaseModel for configuration

- 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.

_docs/02_document/components/06_training/description.md

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+# Component: Training Pipeline
+
+## Overview
+End-to-end YOLOv11 object detection training workflow: dataset formation from augmented annotations, model training, multi-format export (ONNX, TensorRT, RKNN), and encrypted model upload.
+
+**Pattern**: Pipeline / orchestrator
+**Upstream**: Core, Security, API & CDN, Data Models, Data Pipeline (augmented images)
+**Downstream**: None (produces trained models consumed externally)
+
+## Modules
+- `train` — main pipeline: dataset formation → YOLO training → export → upload
+- `exports` — model format conversion (ONNX, TensorRT, RKNN) + upload utilities
+- `manual_run` — ad-hoc developer script for selective pipeline steps
+
+## Internal Interfaces
+
+### train
+```python
+form_dataset() -> None
+copy_annotations(images, folder: str) -> None
+check_label(label_path: str) -> bool
+create_yaml() -> None
+resume_training(last_pt_path: str) -> None
+train_dataset() -> None
+export_current_model() -> None
+```
+
+### exports
+```python
+export_rknn(model_path: str) -> None
+export_onnx(model_path: str, batch_size: int = 4) -> None
+export_tensorrt(model_path: str) -> None
+form_data_sample(destination_path: str, size: int = 500, write_txt_log: bool = False) -> None
+show_model(model: str = None) -> None
+upload_model(model_path: str, filename: str, size_small_in_kb: int = 3) -> None
+```
+
+## Data Access Patterns
+- **Input**: Reads augmented images from `/azaion/data-processed/images/` + labels
+- **Dataset output**: Creates dated dataset at `/azaion/datasets/azaion-{YYYY-MM-DD}/` with train/valid/test splits
+- **Model output**: Saves trained models to `/azaion/models/azaion-{YYYY-MM-DD}/`, copies best.pt to `/azaion/models/azaion.pt`
+- **Upload**: Encrypted model uploaded as split big/small to CDN + API
+- **Corrupted data**: Invalid labels moved to `/azaion/data-corrupted/`
+
+## Implementation Details
+- **Dataset split**: 70% train / 20% valid / 10% test (random shuffle)
+- **Label validation**: `check_label()` verifies all YOLO coordinates are ≤ 1.0
+- **YAML generation**: Writes `data.yaml` with 80 class names (17 actual from classes.json × 3 weather modes, rest as placeholders)
+- **Training config**: YOLOv11 medium (`yolo11m.yaml`), epochs=120, batch=11 (tuned for 24GB VRAM), imgsz=1280, save_period=1, workers=24
+- **Post-training**: Removes intermediate epoch checkpoints, keeps only `best.pt`
+- **Export chain**: `.pt` → ONNX (1280px, batch=4, NMS) → encrypted → split → upload
+- **TensorRT export**: batch=4, FP16, NMS, simplify
+- **RKNN export**: targets RK3588 SoC (OrangePi5)
+- **Concurrent file copying**: ThreadPoolExecutor for parallel image/label copying during dataset formation
+- **`__main__`** in `train.py`: `train_dataset()` → `export_current_model()`
+
+## Caveats
+- Training hyperparameters are hardcoded (not configurable via config file)
+- `old_images_percentage = 75` declared but unused
+- `train.py` imports `subprocess`, `sleep` but doesn't use them
+- `train.py` imports `OnnxEngine` but doesn't use it
+- `exports.upload_model()` creates `ApiClient` with different constructor signature than the one in `api_client.py` — likely stale code
+- `copy_annotations` uses a global `total_files_copied` counter with a local `copied` variable that stays at 0 — reporting bug
+- `resume_training` references `yaml` (the module) instead of a YAML file path in the `data` parameter
+
+## Dependency Graph
+```mermaid
+graph TD
+    constants --> train
+    constants --> exports
+    api_client --> train
+    api_client --> exports
+    cdn_manager --> train
+    cdn_manager --> exports
+    security --> train
+    security --> exports
+    utils --> train
+    utils --> exports
+    dto_annotationClass[dto/annotationClass] --> train
+    inference_onnx[inference/onnx_engine] --> train
+    exports --> train
+    train --> manual_run
+    augmentation --> manual_run
+```
+
+## Logging Strategy
+Print statements for progress (file count, shuffling status, training results). No structured logging.