Update README.md

README.md

@@ -26,6 +26,8 @@ model-index:
       value: 0.8
       name: F1 Score
 library_name: sklearn
+metrics:
+- accuracy
 ---
 
 # Paper Defect Detection
@@ -61,33 +63,4 @@ The model uses a hybrid architecture combining:
 
 - 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}")
-```
+- This was a university project with room for improvement