Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/JackDance/YOLOv8-streamlit-app
🔥🔥🔥 Use streamlit framework to increase yolov8 front-end page interaction function
https://github.com/JackDance/YOLOv8-streamlit-app
classfication detection pose-estimation streamlit tracking yolov8
Last synced: 12 days ago
JSON representation
🔥🔥🔥 Use streamlit framework to increase yolov8 front-end page interaction function
- Host: GitHub
- URL: https://github.com/JackDance/YOLOv8-streamlit-app
- Owner: JackDance
- Created: 2023-05-15T03:32:44.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2023-05-22T01:44:02.000Z (about 1 year ago)
- Last Synced: 2024-02-29T10:35:31.931Z (4 months ago)
- Topics: classfication, detection, pose-estimation, streamlit, tracking, yolov8
- Language: Python
- Homepage:
- Size: 7.9 MB
- Stars: 40
- Watchers: 1
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Lists
- awesome-yolo-object-detection - JackDance/YOLOv8-streamlit-app - streamlit-app?style=social"/> : 🔥🔥🔥 Use streamlit framework to increase yolov8 front-end page interaction function. "知乎「Mr.Luyao」《[深度学习/机器学习项目的前端展示利器--Streamlit](https://zhuanlan.zhihu.com/p/630029493)》"。 (Applications)
README
# 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`~