Update README.md

README.md

@@ -138,72 +138,3 @@ Applied AI  Engineer
 [GitHub Repository](https://github.com/arunimakanavu/anomalydetectionmodel)
 
 ---
-
-
-```yaml
-
-model_name: "Anomaly Detection Model"
-model_type: "autoencoder"
-language: "en"
-license: "mit"
-
-tags:
-  - anomaly-detection
-  - autoencoder
-  - edge-ai
-  - openvino
-  - onnx
-  - computer-vision
-  - unsupervised-learning
-
-task_categories:
-  - anomaly-detection
-  - image-classification
-
-library_name: "pytorch"
-
-datasets:
-  - name: "Casting Product Image Dataset"
-    source: "https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product"
-
-metrics:
-  - name: "Reconstruction Error Threshold"
-    type: "MSE"
-    value: 0.01
-
-model-index:
-  - name: "Anomaly Detection Model"
-    results:
-      - task:
-          type: "anomaly-detection"
-          name: "Casting Defect Detection"
-        dataset:
-          name: "Casting Product Image Dataset"
-          type: "image"
-        metrics:
-          - name: "MSE Reconstruction Error"
-            type: "float"
-            value: 0.01
-
-inference:
-  input_format: "Grayscale image (128x128)"
-  output_format: "Reconstructed image + anomaly score"
-
-intended_use:
-  primary_use: "Industrial defect inspection via anomaly detection."
-  limitations:
-    - "Requires consistent lighting and background conditions."
-    - "Trained specifically on metal casting images."
-
-author:
-  name: "Arunima Surendran"
-  role: "AI Developer & Researcher"
-  repository: "https://github.com/arunimakanavu/aonmalydetectionmodel"
-  email: "N/A"
-
-framework_versions:
-  pytorch: "2.2.0"
-  openvino: "2024.1"
-  onnx: "1.15.0"
-
-```