# YOLOv8-streamlit-app

**Repository Path**: pyxstudy/YOLOv8-streamlit-app

## Basic Information

- **Project Name**: YOLOv8-streamlit-app
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2024-09-13
- **Last Updated**: 2024-11-08

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README


<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.


***

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