Update README.md

README.md

@@ -1,16 +1,3 @@
-# Wheat Anomaly Detection Model
-
-## Overview
-
-This model is designed to detect anomalies in wheat crops, such as disease, pest infection, or nutrient deficiency. The model uses a ResNet50 architecture trained on a balanced dataset of healthy and anomalous wheat images. It is fine-tuned to detect various anomalies and is ideal for agricultural applications.
-
-## Model Details
-
-- **Model Type**: ResNet50 (PyTorch)
-- **Task**: Image Classification / Anomaly Detection
-- **Intended Use**: Detect anomalies in wheat crops using images
-- **License**: Apache-2.0
-
 ## Model Card Metadata
 
 The following YAML configuration is part of the model's metadata to ensure correct pipeline identification on Hugging Face:
@@ -29,3 +16,18 @@ library_name: pytorch
 datasets:
   - your_huggingface_username/your_dataset_name  # Replace with your actual dataset name
 pipeline_tag: image-classification
+
+# Wheat Anomaly Detection Model
+
+## Overview
+
+This model is designed to detect anomalies in wheat crops, such as disease, pest infection, or nutrient deficiency. The model uses a ResNet50 architecture trained on a balanced dataset of healthy and anomalous wheat images. It is fine-tuned to detect various anomalies and is ideal for agricultural applications.
+
+## Model Details
+
+- **Model Type**: ResNet50 (PyTorch)
+- **Task**: Image Classification / Anomaly Detection
+- **Intended Use**: Detect anomalies in wheat crops using images
+- **License**: Apache-2.0
+
+