86 lines
3.3 KiB
Markdown
86 lines
3.3 KiB
Markdown
# Smoke and Fire YOLO
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Ultralytics YOLO training, validation, prediction, and export pipeline for the Smoke-Fire-Detection-YOLO dataset.
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## Project layout
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```text
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configs/datasets/smoke_fire.yaml Dataset configuration
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models/pretrained/yolo11s.pt Default pretrained model
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src/yolo/cli.py Command-line entry point
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src/yolo/config.py Training configuration
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src/yolo/defaults.py Project defaults
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src/yolo/engine.py Train, validate, predict, and export
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src/yolo/checkpoints.py Checkpoint discovery and resume support
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src/yolo/reporting.py Training result reporting
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```
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## Setup
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```bash
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uv sync
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```
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## Commands
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Fine-tune from the current best YOLO11s checkpoint:
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```bash
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uv run fire-yolo train
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```
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Resume from the newest checkpoint:
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```bash
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uv run fire-yolo train --resume
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```
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Validate a trained model:
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```bash
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uv run fire-yolo val --weights runs/detect/<run-name>/weights/best.pt
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```
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Run inference:
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```bash
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uv run fire-yolo predict --weights runs/detect/<run-name>/weights/best.pt --source path/to/image-or-video
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```
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Export a model:
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```bash
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uv run fire-yolo export --weights runs/detect/<run-name>/weights/best.pt --format onnx
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```
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## Default training settings
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- Model: `runs/detect/smoke_fire_yolo11s_v1-4/weights/best.pt`
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- Dataset: `configs/datasets/smoke_fire.yaml`
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- Epochs: 80
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- Image size: 768
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- Batch: 24
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- Workers: 0
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- Optimizer: AdamW
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- Initial learning rate: 0.0002
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- Mosaic/MixUp: disabled
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- Prediction confidence: 0.40
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- Checkpoint interval: every 5 epochs
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- Output: `runs/detect`
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Training writes `last.pt`, `best.pt`, periodic checkpoints, and `best_point.json` to the run directory.
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## Web Detection Service
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After training finishes, install the web dependencies and run the integrated frontend and inference API:
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```bash
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uv sync
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uv run uvicorn backend.main:app --host 127.0.0.1 --port 8000
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```
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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.
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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.
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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.
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