基于yolov8-firedetection的火灾探测部署.zip
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基于yolov8-firedetection的火灾探测部署.zip <div align="center"># YOLOv8 Streamlit APP <p> <a align="center" href="https://ultralytics.com/yolov8" target="_blank"> <img width="50%" src="pic_bed/banner-yolov8.png"></a> </p><br><div> <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a> <a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a> <a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a> <br> <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a> <a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a> </div> <br></div>## IntroductionThis repository supply a user-friendly interactive interface for [YOLOv8](https://github.com/ultralytics/ultralytics) and the interface is powered by [Streamlit](https://github.com/streamlit/streamlit). It could serve as a resource for future reference while working on your own projects.## Features- Feature1: Object detection task.- Feature2: Multiple detection models. `yolov8n`, `yolov8s`, `yolov8m`, `yolov8l`, `yolov8x`- Feature3: Multiple input formats. `Image`, `Video`, `Webcam`## Interactive Interface### Image Input Interface### Video Input Interface### Webcam Input Interface## Installation### Create a new conda environment```commandline# createconda create -n yolov8-streamlit python=3.8 -y# activateconda activate yolov8-streamlit```### Clone repository```commandlinegit clone https://github.com/JackDance/YOLOv8-streamlit-app```### Install packages```commandline# yolov8 dependenciespip install ultralytics# Streamlit dependenciespip install streamlit```### Download Pre-trained YOLOv8 Detection WeightsCreate a directory named `weights` and create a subdirectory named `detection` and save the downloaded YOLOv8 object detection weights inside this directory. The weight files can be downloaded from the table below.| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) || ------------------------------------------------------------------------------------ | --------------------- | -------------------- | ------------------------------ | ----------------------------------- | ------------------ | ----------------- || [YOLOv8n](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n.pt) | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 || [YOLOv8s](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s.pt) | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 || [YOLOv8m](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m.pt) | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 || [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 || [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |## Run```commandlinestreamlit run app.py```Then will start the Streamlit server and open your web browser to the default Streamlit page automatically.## TODO List- Add `Tracking` capability.- Add `Classification` capability.- Add `Pose estimation` capability.***If you also like this project, you may wish to give a `star` (^.^)✨ . If any questions, please raise `issue`~