fabric_defect_detection

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fabric_defect_detection

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# Fabric Defect Detection

This project is a real-time fabric defect detection system that uses a webcam to identify defects in fabric. The application is built with Streamlit and uses a YOLO model for object detection.

## Features

* Real-time defect detection using a webcam.
* Identifies the following defect classes: 'No Defect', 'Hole', 'Stain', 'Misweave'.
* Displays the bounding box and the predicted class with confidence score for the detected defect.
* User-friendly web interface built with Streamlit.

## How to Run

### Prerequisites

* Python 3.x
* Webcam

### Installation

1.  **Clone the repository:**
    ```bash
    git clone [https://github.com/nikhiljose7/fabric_defect_detection.git](https://github.com/nikhiljose7/fabric_defect_detection.git)
    cd fabric_defect_detection
    ```
2.  **Install the dependencies:**
    ```bash
    pip install -r requirements.txt
    ```

The `requirements.txt` file should contain:
    ```
    streamlit
    opencv-python-headless
    ultralytics
    Pillow
    numpy
    ```

## Usage

1.  **Run the Streamlit app:**
    ```bash
    streamlit run app.py
    ```
2.  **Open your web browser** and go to the local URL provided by Streamlit (usually `http://localhost:8501`).
3.  **Click the "Start Camera" checkbox** to begin the real-time defect detection.

## Model

The application uses a pre-trained YOLO model (`best.pt`) to detect fabric defects. The model is trained to identify the following classes:

* No Defect
* Hole
* Stain
* Misweave

fabric_defect_detection

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