AbdullahImran/Paper-Defect-Detection

Paper Defect Detection

Model Description

This model is designed for automated surface defect detection in manufacturing using a hybrid approach that combines classical machine learning and deep learning techniques.

Model Architecture

The model uses a hybrid architecture combining:

• Logistic Regression
• SVM
• Naive Bayes
• CNN
• Ensemble Voting Classifier

Feature Extraction Methods

• Histogram of Oriented Gradients (HOG)
• Gabor Filters
• Canny Edge Detection
• Wavelet Transforms

Performance

Model	Train Accuracy	Test Accuracy
Logistic Regression	0.99	0.79
SVM	0.86	0.80
Ensemble Model	0.90	0.81

Limitations

• Performance may degrade for defect types not represented in the training data
• Variations in lighting or textures can affect classification accuracy
• This was a university project with room for improvement

Usage

from transformers import AutoModelForImageClassification, AutoFeatureExtractor
import torch
from PIL import Image
from torchvision import transforms

model_name = "your-username/surface-defect-detection"
model = AutoModelForImageClassification.from_pretrained(model_name)
feature_extractor = AutoFeatureExtractor.from_pretrained(model_name)

# Preprocess the input image
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor()
])

image = Image.open("path/to/sample-image.jpg")
inputs = feature_extractor(images=image, return_tensors="pt")

# Perform inference
with torch.no_grad():
    outputs = model(**inputs)
    predicted_class = outputs.logits.argmax(-1).item()

print(f"Predicted Defect Class: {predicted_class}")