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README.md

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 # Anomaly Detection Model – Edge AI for Casting Defect Inspection
 
 ## Overview
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 ---
 
+
+```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"
+
+```
+