# Steel ROI Gate Training

This project now supports a **steel-surface detector** in front of defect segmentation.

Why detector and not plain classifier?

1. A classifier can say `steel` or `non_steel`, but it cannot give the bounding box you asked for.
2. A detector can localize the steel region, crop that ROI, and send only that region into the defect segmentation model.
3. This is the right architecture for mixed live-camera scenes with people, tools, and background clutter.

## What the scripts do

`prepare_surface_gate_dataset.py`

- downloads steel-positive sources automatically
- downloads non-steel backgrounds automatically
- creates a YOLO detector dataset
- generates synthetic composites so the detector learns actual steel bounding boxes
- writes `dataset.yaml`

`train_surface_gate_detector.py`

- finetunes a small YOLO detector quickly
- copies the best checkpoint into `models/steel_surface_detector.pt`
- makes the web app use that model automatically after restart

## Automatic data sources

Positive steel sources:

- NEU mirror on Hugging Face
- local `test_images/`
- optional Severstal mirror on Hugging Face

Automatic non-steel/background source:

- `torchvision` datasets downloaded automatically
- default quick path: `CIFAR10`
- optional stronger but heavier path: `STL10`

This avoids making you manually collect a non-steel dataset.

## Recommended quick workflow

1. Install training extras:

```bash
cd "/Users/ravindranadhm/Documents/Projects/steel-surface-inspection copy"
venv/bin/python -m pip install -r training/requirements.txt
```

2. Build the detector dataset:

```bash
cd "/Users/ravindranadhm/Documents/Projects/steel-surface-inspection copy"
venv/bin/python training/prepare_surface_gate_dataset.py
```

This default mode is the fastest path because it avoids the larger Severstal archive and uses `CIFAR10` as an automatic non-steel source.

If you want stronger natural-scene negatives later:

```bash
venv/bin/python training/prepare_surface_gate_dataset.py --negative-source stl10
```

If you want a stronger detector after the first run, rebuild with Severstal included:

```bash
venv/bin/python training/prepare_surface_gate_dataset.py --include-severstal
```

3. Train the lightweight detector:

```bash
cd "/Users/ravindranadhm/Documents/Projects/steel-surface-inspection copy"
venv/bin/python training/train_surface_gate_detector.py
```

4. Restart the app:

```bash
cd "/Users/ravindranadhm/Documents/Projects/steel-surface-inspection copy"
venv/bin/uvicorn api.main:app --host 127.0.0.1 --port 8000
```

## Fast settings

If you want the quickest useful first model:

- `model=yolo11n.pt`
- `epochs=18`
- `imgsz=512`
- `batch=16`
- dataset build without `--include-severstal`

If you have only CPU and want to finish faster:

```bash
venv/bin/python training/train_surface_gate_detector.py --epochs 12 --imgsz 416 --batch 8
```

## If you want a stronger detector later

- increase `--max-severstal`
- increase `--max-negatives`
- raise `--composite-multiplier`
- train `25-40` epochs instead of `12-18`

## Runtime config after training

The app will automatically use:

- `models/steel_surface_detector.pt`

Relevant env keys are already supported:

- `SURFACE_DETECTOR_PATH`
- `SURFACE_DETECTOR_CLASS_NAME`
- `SURFACE_DETECTOR_CONFIDENCE`
- `SURFACE_DETECTOR_MIN_AREA_RATIO`
- `SURFACE_DETECTOR_EXPAND_RATIO`
- `SURFACE_DETECTOR_IMAGE_SIZE`

## Important expectation

The first fast model is meant to be a practical gate, not a perfect production vision model.

The improvement loop should be:

1. train the quick detector
2. deploy it
3. save false accepts / false rejects from live camera
4. add those hard examples back into training
5. retrain

That is the fastest route to a reliable factory-facing steel ROI gate.