AnomalyDetection-MVTech-Metal

Model Card for MetalPart-Anomaly-Detector

This model detects anomalies in metal parts during production processes. It uses Deep Learning and OpenVINO Runtime for high-accuracy anomaly detection, providing heatmaps and segmentation masks for visualizing defects like scratches or deformations.

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Model Details

Model Description

• Developed by: Keyvan Hardani
• Shared by: GitHub Repository
• Model type: Image segmentation and anomaly detection
• License: Apache 2.0
• Finetuned from model: None

Model Sources

• Repository: GitHub Link
• Demo: Hugging Face Demo Link

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Uses

Direct Use

This model is directly usable for:

• Quality Control: Ensuring defect-free metal parts in production.
• Predictive Maintenance: Early detection of anomalies to avoid major breakdowns.
• Automated Inspection: Enhancing efficiency in industrial workflows.

Out-of-Scope Use

This model is not suited for non-industrial materials or environments with highly unstructured data.

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Bias, Risks, and Limitations

Limitations

• Requires high-quality input images with consistent lighting for optimal results.
• Performance may vary depending on the dataset used.

Recommendations

Users should test the model with a subset of their own data before large-scale deployment.

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How to Get Started with the Model

To use this model:

1. Download the pre-trained weights (model.xml, model.bin, and metadata.json) from the repository.
2. Place the model files in the appropriate directory, as described in the GitHub README.

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Training Details

Training Data

• Dataset Used: MVTec AD (metal parts subset)
• Preprocessing: Normalization and resizing to model-specific input dimensions.

Training Procedure

• Framework: OpenVINO Runtime
• Loss Function: Cross-Entropy Loss
• Optimizer: Adam

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Evaluation

Metrics

• AUROC: Measures the model's ability to distinguish between anomalous and normal parts.
• F1 Score: Assesses the balance between precision and recall.

Results

• Image AUROC: 0.95
• Image F1 Score: 0.94
• Pixel AUROC: 0.96
• Pixel F1 Score: 0.71

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Environmental Impact

• Hardware Type: GPU-based training and inference (NVIDIA RTX 4080)
• Hours used: Approx. 10 hours
• Carbon Emitted: [Estimate pending]

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Citation

If you use this model, please cite it as:

@misc {keyvan_hardani_2024, author = { {Keyvan Hardani} }, title = { AnomalyDetection-MVTech-Metal (Revision b326b4e) }, year = 2024, url = { https://huggingface.co/Keyven/AnomalyDetection-MVTech-Metal }, doi = { 10.57967/hf/3678 }, publisher = { Hugging Face } }

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Model Card Authors

• Keyvan Hardani

Contact

For questions or support, please reach out via GitHub Issues