Update README.md

README.md

@@ -1,39 +1,40 @@
----
-model_name: "Wheat Anomaly Detection Model"
-tags:
-  - pytorch
-  - resnet
-  - agriculture
-  - anomaly-detection
-license: apache-2.0
-library_name: pytorch
-
-# Wheat Anomaly Detection Model
-
-This model is a PyTorch-based ResNet model trained to detect anomalies in wheat crops, such as diseases, pests, and nutrient deficiencies.
-
-## How to Load the Model
-
-To load the trained model, use the following code:
-
-```python
-from transformers import AutoModelForImageClassification
-import torch
-
-# Load the pre-trained model
-model = AutoModelForImageClassification.from_pretrained('your_huggingface_username/your_model_name')
-
-# Put the model in evaluation mode
-model.eval()
-
-# Example of making a prediction
-image_path = "path_to_your_image.jpg"  # Replace with your image path
-image = Image.open(image_path)
-inputs = transform(image).unsqueeze(0)  # Apply the necessary transformations to the image
-inputs = inputs.to(device)
-
-# Make a prediction
-with torch.no_grad():
-    outputs = model(inputs)
-    predicted_class = torch.argmax(outputs.logits, dim=1)
-    print(f"Predicted Class: {predicted_class.item()}")
+---
+model_name: "Wheat Anomaly Detection Model"
+tags:
+  - pytorch
+  - resnet
+  - agriculture
+  - anomaly-detection
+license: apache-2.0
+library_name: pytorch
+---
+
+# Wheat Anomaly Detection Model
+
+This model is a PyTorch-based ResNet model trained to detect anomalies in wheat crops, such as diseases, pests, and nutrient deficiencies.
+
+## How to Load the Model
+
+To load the trained model, use the following code:
+
+```python
+from transformers import AutoModelForImageClassification
+import torch
+
+# Load the pre-trained model
+model = AutoModelForImageClassification.from_pretrained('your_huggingface_username/your_model_name')
+
+# Put the model in evaluation mode
+model.eval()
+
+# Example of making a prediction
+image_path = "path_to_your_image.jpg"  # Replace with your image path
+image = Image.open(image_path)
+inputs = transform(image).unsqueeze(0)  # Apply the necessary transformations to the image
+inputs = inputs.to(device)
+
+# Make a prediction
+with torch.no_grad():
+    outputs = model(inputs)
+    predicted_class = torch.argmax(outputs.logits, dim=1)
+    print(f"Predicted Class: {predicted_class.item()}")