https://github.com/vansh-khaneja/in-store-analytics-using-object-detection
This repository will guide you over how to create an object detection model that can count no. of people at a store in real time using YOLOv8 model for detection people around
https://github.com/vansh-khaneja/in-store-analytics-using-object-detection
machine-learning object-detection opencv python yolov8
Last synced: 2 months ago
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This repository will guide you over how to create an object detection model that can count no. of people at a store in real time using YOLOv8 model for detection people around
- Host: GitHub
- URL: https://github.com/vansh-khaneja/in-store-analytics-using-object-detection
- Owner: vansh-khaneja
- Created: 2024-07-29T13:55:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-29T15:03:50.000Z (almost 2 years ago)
- Last Synced: 2025-06-07T14:03:40.359Z (about 1 year ago)
- Topics: machine-learning, object-detection, opencv, python, yolov8
- Language: Python
- Homepage:
- Size: 246 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# In-Store Analytics using Object Detection
This project implements Object Detection for performing In-Store Analytics using `yolov8n` as the per-trained model. This can help in getting using insights for the store manager to analyze and boost up yhe sales by monitoring the crowd at a particular instance in the store. To learn more about the project please refer this [article](link).
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Execution](#execution)
- [Contact](#contact)
## Introduction
In this project, we used YOLOv8 model by [Ultralytics](https://docs.ultralytics.com/) for efficiently detecting customers from the video frames in real-time. This can help in getting useful data for the store manager to increase sales.
## Features
- Fast and efficient method
- Uses `yolov8n` model
- Real time support
- Optimizable to increase accuarcy
## Installation
1. Clone the repository:
```sh
git clone https://github.com/vansh-khaneja/In-Store-Analytics-using-Object-Detection
cd In-Store-Analytics-using-Object-Detection
```
2. Set up the Python environment and install dependencies:
```sh
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
```
## Execution
1.Download the sample Video Dataset for this project [here](https://www.pexels.com/video/people-walking-inside-a-shopping-mall-4750076/) or you can try with your own dataset. Just change the path of the Video here.
```sh
input_video_path = "c:/Users/testing.mp4"
```
2.You can also change the model based on the use case. Here in this project we have used ```yolov8n.pt``` model. Please refer [Ultraytics documentation](https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes) for learning about different model sizes. You can change the model here.
```sh
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
```
3.Execute the ```main.py``` file by running this command in terminal.
```sh
python main.py
```
## Contact
For any questions or issues, feel free to open an issue on this repository or contact me at vanshkhaneja2004@gmail.com.
Happy coding!