基于yolov8-firedetection的火灾探测部署.zip

2401_89451588基于yolov8-firedetection的火灾探测部署.zip 19.81MB

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基于yolov8-firedetection的火灾探测部署.zip 大约有10个文件

yolov8_firedetection-main/.gitignore 54B
yolov8_firedetection-main/app.py 1.99KB
yolov8_firedetection-main/config.py 1.25KB
yolov8_firedetection-main/README.md 4.9KB
yolov8_firedetection-main/requirements.txt 111B
yolov8_firedetection-main/utils.py 5.2KB
yolov8_firedetection-main/weights/detection/best_train.pt 21.49MB
yolov8_firedetection-main/__pycache__/config.cpython-310.pyc 945B
yolov8_firedetection-main/__pycache__/utils.cpython-310.pyc 4.26KB
一个基于yolov8的火灾检测部署_yolov8_firedetection/项目内附说明/如果解压失败请用ara软件解压.txt 42B

资源介绍:

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> ## Introduction This 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 ![image_input_demo](https://github.com/JackDance/YOLOv8-streamlit-app/blob/master/pic_bed/image_input_demo.png) ### Video Input Interface ![video_input_demo](https://github.com/JackDance/YOLOv8-streamlit-app/blob/master/pic_bed/video_input_demo.png) ### Webcam Input Interface ![webcam_input_demo](https://github.com/JackDance/YOLOv8-streamlit-app/blob/master/pic_bed/webcam_input_demo.png) ## Installation ### Create a new conda environment ```commandline # create conda create -n yolov8-streamlit python=3.8 -y # activate conda activate yolov8-streamlit ``` ### Clone repository ```commandline git clone https://github.com/JackDance/YOLOv8-streamlit-app ``` ### Install packages ```commandline # yolov8 dependencies pip install ultralytics # Streamlit dependencies pip install streamlit ``` ### Download Pre-trained YOLOv8 Detection Weights Create 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 ```commandline streamlit 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`~