# YOLOv8-Fire-and-Smoke-Detection

**Repository Path**: anold/YOLOv8-Fire-and-Smoke-Detection

## Basic Information

- **Project Name**: YOLOv8-Fire-and-Smoke-Detection
- **Description**: No description available
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2024-08-30
- **Last Updated**: 2025-07-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Fire and Smoke Tracking and Detection using YOLOv8
## Introduction

This repository contains the code for tracking and detecting fires and smokes in real-time video using YOLOv8. The project uses a pre-trained YOLOv8 model to identify the presence of fire and smoke in a given video frame and track it through subsequent frames. 



https://user-images.githubusercontent.com/22887323/216410880-297f4408-8d22-47a1-b894-6f3f3d8109fb.mp4


## The following packages are required to run the code
    ultralytics
    roboflow
    CUDA (if using GPU for acceleration)

## Steps to train on custom dataset
1. Install YOLOv8
2. CLI Basics
3. Inference with Pre-trained COCO Model
4. Roboflow Universe
5. Preparing a custom dataset
6. Custom Training
7. Validate Custom Model
8. Inference with Custom Model

## Custom data
Used roboflow to  annotate fire and smoke images.
Sample notebook show how we can  add the Roboflow workflow project using API to download the annotated dataset to train the model.
 Use the  below code to download the datset:

    from roboflow import Roboflow
    rf = Roboflow(api_key="xxxxxxxxxxxxxxxx")
    project = rf.workspace("custom-thxhn").project("fire-wrpgm")
    dataset = project.version(8).download("yolov8")

## Evaluation
The below chart show  the loss , mAP (mean Average Precision) score for the train, test,validation set.

![alt text](/runs/detect/train/results.png)

### confusion Matrix : 
![alt text](/runs/detect/train/confusion_matrix.png)

##  Inference 
Run the  model using below command:

`!yolo task=detect mode=predict model=<path to weight file> conf=0.25 source=<path to source image or  video> save=True`

The --source argument is required to specify the path to the input video. the above command save your weight in run/predict, which will contain the annotated frames with fire and smoke detections.

## Result
![alt text](/runs/detect/train/val_batch0_pred.jpg)

The project can detect fire and smoke in real-time video with high accuracy. The detection and tracking performance can be improved by fine-tuning the YOLOv8 model on a custom dataset.
It can be used as a starting point for more advanced projects and can be easily integrated into a larger system for fire and smoke monitoring.


## References

- [YOLOv8 paper](https://arxiv.org/abs/2004.10934)
- [YOLOv8 PyTorch implementation](https://github.com/ultralytics/yolov5)
- [YOLOv8 weights conversion script](https://github.com/ultralytics/yolov5/blob/master/weights/convert.py)