metadata

language:
  - en
tags:
  - defect-detection
  - image-classification
  - machine-learning
  - quality-control
  - ensemble-learning
  - neural-networks
license: apache-2.0
datasets:
  - custom_paper_surface_defect
pipeline_tag: image-classification
model-index:
  - name: Paper Defect Detection
    results:
      - task:
          type: image-classification
          name: Surface Defect Detection
        metrics:
          - type: accuracy
            value: 0.81
            name: Ensemble Test Accuracy
          - type: f1
            value: 0.8
            name: F1 Score
library_name: sklearn

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}")