| # model_card.yaml | |
| model_name: "AONomaly 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: "AONomaly 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" | |