README.md

metadata

title: Innomium Ember
emoji: 🔥
colorFrom: red
colorTo: yellow
sdk: static
app_file: index.html
pinned: false
license: apache-2.0

Innomium Ember

Ultra-light YOLO fire, smoke, and extinguisher detection for safety monitoring and edge vision.

90% accuracy · 3 classes · 9.8 MB ONNX · Runs in browser & on edge

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Overview

Innomium Ember is a cutting-edge YOLO-based fire hazard detector — very light, very strong, and built for real-world safety scenes.

Detect fire, smoke, and fire extinguisher with a compact ~9.8 MB ONNX model that runs on CPU, edge hardware, or directly in the browser.

Classes	fire · smoke · fire extinguisher
Accuracy	90%
Model format	ONNX
Model size	~9.8 MB
Input size	640 × 640
Inference	Browser (WASM) · Python · Edge CPU/GPU
Post-processing	Per-class NMS · Smoke merge · Color-prior filters

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Performance

Ember classifies fire hazards in outdoor and industrial scenes with per-class bounding boxes and confidence scores.

Input	Ember Output
Raw camera frame	Class labels + confidence scores

What this demonstrates:

• All three hazard classes in one scene: fire, smoke, and fire extinguisher
• Per-class NMS without cross-class suppression
• Flame detections up to 77% alongside smoke (36%) and extinguisher (31%)
• Suitable for outdoor events, safety drills, warehouses, and industrial sites

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Key Features

• Multi-class YOLO — Fire, smoke, and fire extinguisher with class ID remapping from the ONNX head.
• Per-class NMS — Hard NMS applied independently per class to preserve overlapping hazard types.
• Smoke merge — Fragmented smoke boxes merge into coherent plumes for cleaner alerts.
• Color-prior filters — Borderline fire and extinguisher detections are validated against expected pixel appearance.
• Privacy-first demo — The live Space runs inference entirely in your browser. No frames are uploaded.

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Use Cases

Sector	Application
Industrial	Warehouse and factory fire/smoke monitoring
Commercial	Kitchen, server room, and office hazard detection
Outdoor	Wildfire perimeter, campsite, and bonfire flame detection
Safety	Fire extinguisher location verification in camera views

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Live Demo

Try Ember directly in your browser:

https://huggingface.co/spaces/innomium/fire-detection

1. Open the Space
2. Upload an image or click Load Example
3. View detections with class labels and confidence scores

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Repository Structure

├── index.html              # Marketing site + interactive demo
├── main.js / detector.js   # Browser inference (ONNX Runtime Web)
├── weights.onnx              # ONNX model weights (Git LFS)
├── app.py                    # Python FireDetector for batch / server use
├── example_input.png         # Sample input frame
├── example_output.png        # Sample detection output
└── innomium_icon.svg         # Innomium logo

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Local Python Usage

pip install -r requirements.txt

from pathlib import Path
import cv2
from app import FireDetector

detector = FireDetector(Path("."))
image = cv2.imread("example_input.png")
boxes = detector.predict_image(image)
for box in boxes:
    print(detector.class_names[box.cls_id], box.conf)

Batch inference

results = detector.predict_batch([image], offset=0, n_keypoints=0)
for frame in results:
    print(frame.frame_id, len(frame.boxes))

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Model Pipeline

1. Letterbox preprocess — Resize and pad to model input (640×640)
2. ONNX inference — YOLO multi-class fire hazard detection
3. Class remap — Map model head order to [fire, smoke, fire extinguisher]
4. Per-class NMS — Independent hard NMS per hazard class
5. Smoke merge & fire suppress — Merge fragmented smoke; suppress nested fire duplicates
6. Color filters — Validate borderline fire/extinguisher boxes against pixel appearance

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About Innomium

Innomium builds cutting-edge computer vision models for mission-critical environments — where accuracy, latency, and deployability all matter.

Very light. Very strong. Built for safety.

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License

Apache 2.0