YOLOv8 Streamlit APP

Introduction

This repository supply a user-friendly interactive interface for YOLOv8 and the interface is powered by Streamlit. It could serve as a resource for future reference while working on your own projects.

Features

• Feature1: Object detection task, segment task, pose task.

• Feature2:

        Multiple detection models. `yolov8n`, `yolov8s`, `yolov8m`, `yolov8l`, `yolov8x`
        Multiple segment models. `yolov8n-seg`, `yolov8s-seg`, `yolov8m-seg`, `yolov8l-seg`, `yolov8x-seg`
        Multiple pose models. `yolov8n-pose`, `yolov8s-pose`, `yolov8m-pose`, `yolov8l-pose`, `yolov8x-pose`
  

• Feature3: Multiple input formats. Image, Video, Webcam

Interactive Interface

Image Input Interface

Video Input Interface

Webcam Input Interface

Installation

Create a new conda environment

# create
conda create -n yolov8-streamlit python=3.8 -y

# activate
conda activate yolov8-streamlit

Clone repository

git clone https://github.com/chenanga/YOLOv8-streamlit-app

Install packages

# 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.

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Model	size (pixels)	mAPval 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n	640	37.3	80.4	0.99	3.2	8.7
YOLOv8s	640	44.9	128.4	1.20	11.2	28.6
YOLOv8m	640	50.2	234.7	1.83	25.9	78.9
YOLOv8l	640	52.9	375.2	2.39	43.7	165.2
YOLOv8x	640	53.9	479.1	3.53	68.2	257.8

Model	size (pixels)	mAPbox 50-95	mAPmask 50-95	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B)
YOLOv8n-seg	640	36.7	30.5	96.1	1.21	3.4	12.6
YOLOv8s-seg	640	44.6	36.8	155.7	1.47	11.8	42.6
YOLOv8m-seg	640	49.9	40.8	317.0	2.18	27.3	110.2
YOLOv8l-seg	640	52.3	42.6	572.4	2.79	46.0	220.5
YOLOv8x-seg	640	53.4	43.4	712.1	4.02	71.8	344.1

Model	size (pixels)	acc top1	acc top5	Speed CPU ONNX (ms)	Speed A100 TensorRT (ms)	params (M)	FLOPs (B) at 640
YOLOv8n-cls	224	66.6	87.0	12.9	0.31	2.7	4.3
YOLOv8s-cls	224	72.3	91.1	23.4	0.35	6.4	13.5
YOLOv8m-cls	224	76.4	93.2	85.4	0.62	17.0	42.7
YOLOv8l-cls	224	78.0	94.1	163.0	0.87	37.5	99.7
YOLOv8x-cls	224	78.4	94.3	232.0	1.01	57.4	154.8

Run

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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Fork from https://github.com/JackDance/YOLOv8-streamlit-app