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_tasks/AZ-161_test_onnx_inference.md

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+# ONNX Inference Tests
+
+**Task**: AZ-161_test_onnx_inference
+**Name**: ONNX Inference Tests
+**Description**: Implement 4 tests for ONNX model loading, inference execution, postprocessing, and CPU latency
+**Complexity**: 3 points
+**Dependencies**: AZ-152_test_infrastructure
+**Component**: Blackbox Tests
+**Jira**: AZ-161
+**Epic**: AZ-151
+
+## Problem
+
+The ONNX inference engine loads a model, runs detection on images, and postprocesses results. Tests must verify the full pipeline works on CPU (smoke test — no precision validation).
+
+## Outcome
+
+- 4 passing pytest tests
+- Blackbox tests in `tests/test_onnx_inference.py`
+- Performance test in `tests/performance/test_inference_perf.py`
+
+## Scope
+
+### Included
+- BT-INF-01: Model loads successfully (no exception, valid engine)
+- BT-INF-02: Inference returns output (array shape [batch, N, 6+])
+- BT-INF-03: Postprocessing returns valid detections (x,y,w,h ∈ [0,1], cls ∈ [0,79], conf ∈ [0,1])
+- PT-INF-01: ONNX inference latency (single image ≤ 10s on CPU)
+
+### Excluded
+- TensorRT inference (requires NVIDIA GPU)
+- Detection precision/recall validation (smoke-only per user decision)
+
+## Acceptance Criteria
+
+**AC-1: Model loads**
+Given azaion.onnx bytes
+When OnnxEngine(model_bytes) is constructed
+Then no exception; engine has valid input_shape and batch_size
+
+**AC-2: Inference output**
+Given ONNX engine + 1 preprocessed image
+When engine.run(input_blob) is called
+Then returns list of numpy arrays; first array has shape [batch, N, 6+]
+
+**AC-3: Valid detections**
+Given ONNX engine output from real image
+When Inference.postprocess() is called
+Then returns list of Detection objects; each has x,y,w,h ∈ [0,1], cls ∈ [0,79], confidence ∈ [0,1]
+
+**AC-4: CPU latency**
+Given 1 preprocessed image + ONNX model
+When single inference runs
+Then completes within 10 seconds
+
+## Constraints
+
+- Uses onnxruntime (CPU) not onnxruntime-gpu
+- ONNX model is 77MB, loaded once (session fixture)
+- Image preprocessing must match model input size (1280×1280)
+- Performance test marked: `@pytest.mark.performance`
+- This is a smoke test — validates structure, not detection accuracy