Fabric Defect Detection

This project implements a fabric defect detection system using Streamlit for a user-friendly interface and YOLOv8 for object detection.

Project Overview

Manual fabric defect inspection is time-consuming and prone to human error. This project addresses this challenge by offering an automated solution for fabric quality control.

Features

• Upload fabric images for defect detection.
• Visualize the detected defects with labels.

Prerequisites

• Python 3.6 or later
• Streamlit
• YOLOv8 (https://github.com/topics/yolov8)

Installation

1. Clone this repository.
2. Create a new virtual environment (recommended).
3. Install required dependencies:

   pip install streamlit yolov8

4. Download pre-trained YOLOv8 weights (compatible with your dataset) and place them in the project directory.

Usage

1. Run the application:

   streamlit run main.py

2. Access the application in your web browser at http://localhost:8501.
3. Upload a fabric image and the model will predict and visualize any detected defects.