Weld Defect Classification System by Chirag PatankarWeld Defect Classification System by Chirag Patankar

Weld Defect Classification System

Chirag Patankar

Completed work

AI Engineer

PyTorch

Streamlit

Manufacturing

Weld Defect Classification

Overview

This project is a deep learning-based system for classifying weld defects using convolutional neural networks (CNNs). The system processes weld images and predicts whether a weld is good or defective, categorizing defects into six different classes.

Features

Trainable Model: Uses PyTorch to train a CNN on labeled weld images.

Testing Module: Evaluates model accuracy using test images.

Web Interface: A Streamlit-powered UI for real-time image classification.

Binary and Multi-Class Classification: Can classify welds as Good or Defective, and further categorize defects into specific types.

Installation

Prerequisites

Ensure you have Python 3.8+ installed. You also need the following dependencies:

pip install torch torchvision streamlit numpy pandas matplotlib pillow

Dataset

The dataset used for training and testing is available on Google Drive. You can download it using the link below:

Download Dataset

Usage

Training the Model

To train the model, run:

python train.py

This will load training images, process them, and train a CNN model. The trained model is saved as modelv5.pt.

Testing the Model

After training, evaluate the model using:

python test.py

This script loads test images and prints evaluation metrics such as accuracy.

Running the Web App

To launch the Streamlit UI for classifying weld images, run:

streamlit run app.py

This will start a web-based interface where users can upload images and receive predictions.

Project Structure

├── data/                  # Directory for training and test datasets
├── train.py               # Training script
├── test.py                # Testing script
├── app.py                 # Streamlit UI for classification
├── modelv5.pt             # Trained model file (generated after training)
└── README.md              # Project documentation

Model Details

CNN architecture with multiple convolutional, batch normalization, dropout, and ReLU layers.

Uses cross-entropy loss and Adam optimizer for training.

Outputs a classification prediction with probability scores.

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks (CNNs) are a class of deep learning models specifically designed for image processing tasks. They consist of multiple layers, including:

Convolutional Layers: Extract spatial features from images by applying filters.

Pooling Layers: Reduce dimensionality and retain essential information.

Fully Connected Layers: Combine extracted features to make classification decisions.

Activation Functions (e.g., ReLU): Introduce non-linearity to improve learning capability.

CNNs are effective for weld defect classification as they can learn to recognize intricate patterns in weld images, distinguishing between different defect types accurately.