vehicle-Counting-with-yolov8

Introduction

This repository supply a user-friendly interactive interface for YOLOv8 with vehicle Tracking and Counting capability. The interface is powered by Streamlit.

Installation

Create a virtual environment

# create
python -m venv yolov8-mot-streamlit

# activate
source yolov8-mot-streamlit/bin/activate

Install packages

# Streamlit dependencies
pip install streamlit

# YOLOv8 dependecies
pip install -e '.[dev]'

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

Run

streamlit run app.py

Then will start the Streamlit server and open your web browser to the default Streamlit page automatically. For vehicle Counting, you can choose "Video" from "Select Source" combo box and use "test3.mp4" inside videos folder as an example.

Result in Colab

Result in App