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 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
# YOLOv8 Streamlit APP
## 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
### Video Input Interface
### Webcam Input Interface
## 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
(pixels) | mAPval
50-95 | Speed
CPU ONNX
(ms) | Speed
A100 TensorRT
(ms) | params
(M) | FLOPs
(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`~
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