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--- |
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language: |
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- en |
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license: mit |
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library_name: pytorch |
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tags: |
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- autoencoder |
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- anomaly-detection |
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- computer-vision |
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- manufacturing |
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- onnx |
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- openvino |
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- edge-ai |
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pipeline_tag: image-classification |
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base_model: custom/autoencoder |
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datasets: |
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- custom |
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metrics: |
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- reconstruction-error |
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--- |
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# Anomaly Detection Model – Edge AI for Casting Defect Inspection |
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## Overview |
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The **Anomaly Detection Model** is an **autoencoder-based anomaly detection system** fine-tuned for industrial **casting defect inspection**. It identifies whether a metal casting image is *normal (OK)* or *defective* by reconstructing input images and analyzing reconstruction errors. |
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This model is designed for **Edge AI deployment**, optimized via **ONNX** and **OpenVINO IR** formats to run efficiently on low-power Intel edge devices. |
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--- |
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## Model Details |
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- **Architecture:** Convolutional Autoencoder |
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- **Framework:** PyTorch |
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- **Training Objective:** Minimize reconstruction loss (MSE) for normal samples |
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- **Optimization:** ONNX and OpenVINO IR export for edge inference |
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- **Task:** Unsupervised anomaly detection |
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- **Domain:** Industrial visual inspection |
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--- |
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## Repository Structure |
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``` |
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├── casting_autoencoder.pth # Trained PyTorch model |
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├── casting_autoencoder.onnx # ONNX export |
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├── model.bin # OpenVINO IR model (bin) |
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├── model.xml # OpenVINO IR model (xml) |
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├── model_card.yaml |
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├── requirements.txt # Dependencies |
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├── inference.py # inference code |
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└── README.md # Model card (this file) |
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``` |
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--- |
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## Dataset |
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**Dataset:** Casting Product Image Dataset (Kaggle) |
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- **Classes:** Defective / Normal |
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- **Modality:** Grayscale industrial images |
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- **Training Strategy:** Only *normal* samples used for training the autoencoder. |
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--- |
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## Key Configuration Parameters |
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- **Image Size**: 304×304 pixels |
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- **Batch Size**: 32 |
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- **Learning Rate**: 1e-3 |
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- **Epochs**: 10 |
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- **Loss Function**: MSE Loss |
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- **Optimizer**: Adam |
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## Model Outputs |
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The training script generates: |
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- `casting_autoencoder.pth` - PyTorch model weights |
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- `casting_autoencoder.onnx` - ONNX export for deployment |
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- Calibrated anomaly threshold based on defective samples |
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## Anomaly Detection Process |
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1. **Training Phase**: Model learns to reconstruct normal casting images |
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2. **Threshold Calibration**: Uses defective samples to determine optimal threshold |
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3. **Inference**: Images with reconstruction error > threshold are flagged as defective |
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## Performance |
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- **Final Training Loss**: 0.0005 |
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- **Suggested Threshold**: 0.0004 |
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- **Model Type**: Unsupervised anomaly detection |
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- **Architecture**: Convolutional Autoencoder |
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## Applications |
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This model is designed for: |
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- Quality control in metal casting manufacturing |
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- Real-time defect detection on production lines |
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- Automated visual inspection systems |
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- Edge deployment in industrial environments |
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## Model Features |
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- **Unsupervised Learning**: Trained only on normal samples |
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- **Real-time Capable**: Optimized for edge deployment |
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- **ONNX Compatible**: Ready for production deployment |
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- **Automatic Thresholding**: Self-calibrating anomaly detection |
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- **Industrial Grade**: Tested on real manufacturing data |
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## Technical Details |
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The model uses a symmetric encoder-decoder architecture with: |
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- Stride-2 convolutions for downsampling |
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- Transposed convolutions for upsampling |
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- ReLU activation in hidden layers |
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- Sigmoid output activation for pixel reconstruction. |
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--- |
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## Export & Deployment |
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| Format | Purpose | |
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|---------|----------| |
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| `.pth` | Original PyTorch model | |
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| `.onnx` | Framework-independent inference | |
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| `.xml` / `.bin` | OpenVINO IR format for edge devices | |
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**Edge Optimization:** Model converted and optimized using `openvino.convert_model()`. |
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--- |
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## Intended Use |
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- Automated visual inspection for manufacturing/QA systems. |
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- Real-time edge deployment in industrial environments. |
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**Not recommended for:** |
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- Non-industrial datasets. |
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- Scenarios with significant domain drift (e.g., lighting changes or non-casting objects). |
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--- |
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## Limitations |
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- Accuracy depends on lighting and background consistency. |
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- Model trained primarily on grayscale casting images. |
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- Thresholds for anomaly detection must be tuned for specific deployment environments. |
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--- |
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## License |
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This project is released under the MIT License. |
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--- |
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## Author |
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**Arunima Surendran** |
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Applied AI Engineer |
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[GitHub Repository](https://github.com/arunimakanavu/anomalydetectionmodel) |
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--- |
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