# 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.