Create README.md

README.md

@@ -0,0 +1,92 @@
+---
+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: 0.80
+            name: F1 Score
+---
+
+# Surface Defect Detection and Classification Model
+
+## 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
+
+```python
+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}")
+```