# Smoke and Fire YOLO

Ultralytics YOLO training, validation, prediction, and export pipeline for the Smoke-Fire-Detection-YOLO dataset.

## Project layout

```text
configs/datasets/smoke_fire.yaml  Dataset configuration
models/pretrained/yolo11s.pt      Default pretrained model
src/yolo/cli.py                   Command-line entry point
src/yolo/config.py                Training configuration
src/yolo/defaults.py              Project defaults
src/yolo/engine.py                Train, validate, predict, and export
src/yolo/checkpoints.py           Checkpoint discovery and resume support
src/yolo/reporting.py             Training result reporting
```

## Setup

```bash
uv sync
```

## Commands

Fine-tune from the current best YOLO11s checkpoint:

```bash
uv run fire-yolo train
```

Resume from the newest checkpoint:

```bash
uv run fire-yolo train --resume
```

Validate a trained model:

```bash
uv run fire-yolo val --weights runs/detect/<run-name>/weights/best.pt
```

Run inference:

```bash
uv run fire-yolo predict --weights runs/detect/<run-name>/weights/best.pt --source path/to/image-or-video
```

Export a model:

```bash
uv run fire-yolo export --weights runs/detect/<run-name>/weights/best.pt --format onnx
```

## Default training settings

- Model: `runs/detect/smoke_fire_yolo11s_v1-4/weights/best.pt`
- Dataset: `configs/datasets/smoke_fire.yaml`
- Epochs: 80
- Image size: 768
- Batch: 24
- Workers: 0
- Optimizer: AdamW
- Initial learning rate: 0.0002
- Mosaic/MixUp: disabled
- Prediction confidence: 0.40
- Checkpoint interval: every 5 epochs
- Output: `runs/detect`

Training writes `last.pt`, `best.pt`, periodic checkpoints, and `best_point.json` to the run directory.
## Web Detection Service

After training finishes, install the web dependencies and run the integrated frontend and inference API:

```bash
uv sync
uv run uvicorn backend.main:app --host 127.0.0.1 --port 8000
```

Open `http://127.0.0.1:8000` to use the fire prevention management platform. It provides a system overview, local-video inspection, alert event handling, a risk register, robot channel status, and effective model settings. The browser plays selected videos locally and sends sequential JPEG frames to `POST /api/detect`; requests do not overlap.

Management endpoints include `GET /api/dashboard`, `GET /api/events`, and `PATCH /api/events/{event_id}`. Alert events can be marked as pending, acknowledged, or resolved. The current implementation retains the most recent 500 events in process memory, so events are cleared when the API service restarts.

The local `.env` file contains optional robot settings. Set `WECHAT_WEBHOOK_URL` for an Enterprise WeChat group robot, `FEISHU_WEBHOOK_URL` for a Feishu custom group robot, or both. If Feishu signature verification is enabled, also set `FEISHU_SECRET`. Alerts require three consecutive positive frames by default and use separate 60-second cooldowns for fire and smoke. `ALERT_CONFIRM_FRAMES` and `ALERT_COOLDOWN_SECONDS` override these settings. Without a webhook, video detection still works and the UI reports that notifications are disabled.