# 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`, `PATCH /api/events/{event_id}`, and `GET /api/robots`. Alert events can be marked as pending, acknowledged, or resolved. The robot endpoint reports Enterprise WeChat and Feishu configuration and delivery statistics without exposing webhook credentials. Use `POST /api/robots/{channel}/test` to send a connection test to a configured group robot. The current implementation retains the most recent 500 events and robot delivery counters in process memory, so they 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.