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
- Downloads last month
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Evaluation results
- Ensemble Test Accuracyself-reported0.810
- F1 Scoreself-reported0.800
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("AbdullahImran/Paper-Defect-Detection", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html