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--- |
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license: apache-2.0 |
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datasets: |
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- mvtec-ad |
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metrics: |
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- auroc |
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- f1 |
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pipeline_tag: image-segmentation |
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tags: |
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- anomaly-detection |
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- industrial-inspection |
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- mvtec-ad |
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- deep-learning |
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- openvino |
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- quality-control |
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library_name: openvino |
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--- |
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# Model Card for MetalPart-Anomaly-Detector |
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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 |
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### Model Description |
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- **Developed by:** Keyvan Hardani |
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- **Shared by:** [GitHub Repository](https://github.com/Keyvanhardani/Anomaly-Detection-Metal) |
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- **Model type:** Image segmentation and anomaly detection |
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- **License:** Apache 2.0 |
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- **Finetuned from model:** None |
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### Model Sources |
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- **Repository:** [GitHub Link](https://github.com/Keyvanhardani/Anomaly-Detection-Metal) |
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- **Demo:** [Hugging Face Demo Link](https://huggingface.co/spaces) |
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## Uses |
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### Direct Use |
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This model is directly usable for: |
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- **Quality Control**: Ensuring defect-free metal parts in production. |
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- **Predictive Maintenance**: Early detection of anomalies to avoid major breakdowns. |
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- **Automated Inspection**: Enhancing efficiency in industrial workflows. |
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### Out-of-Scope Use |
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This model is not suited for non-industrial materials or environments with highly unstructured data. |
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## Bias, Risks, and Limitations |
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### Limitations |
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- Requires high-quality input images with consistent lighting for optimal results. |
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- Performance may vary depending on the dataset used. |
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### Recommendations |
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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 |
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To use this model: |
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1. Download the pre-trained weights (`model.xml`, `model.bin`, and `metadata.json`) from the repository. |
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2. Place the model files in the appropriate directory, as described in the [GitHub README](https://github.com/Keyvanhardani/Anomaly-Detection-Metal). |
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## Training Details |
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### Training Data |
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- **Dataset Used:** MVTec AD (metal parts subset) |
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- **Preprocessing:** Normalization and resizing to model-specific input dimensions. |
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### Training Procedure |
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- Framework: OpenVINO Runtime |
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- Loss Function: Cross-Entropy Loss |
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- Optimizer: Adam |
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## Evaluation |
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### Metrics |
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- **AUROC:** Measures the model's ability to distinguish between anomalous and normal parts. |
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- **F1 Score:** Assesses the balance between precision and recall. |
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### Results |
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- **Image AUROC:** 0.95 |
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- **Image F1 Score:** 0.94 |
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- **Pixel AUROC:** 0.96 |
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- **Pixel F1 Score:** 0.71 |
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## Environmental Impact |
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- **Hardware Type:** GPU-based training and inference (NVIDIA RTX 4080) |
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- **Hours used:** Approx. 10 hours |
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- **Carbon Emitted:** [Estimate pending] |
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## Citation |
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If you use this model, please cite it as: |
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@misc {keyvan_hardani_2024, |
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author = { {Keyvan Hardani} }, |
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title = { AnomalyDetection-MVTech-Metal (Revision b326b4e) }, |
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year = 2024, |
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url = { https://huggingface.co/Keyven/AnomalyDetection-MVTech-Metal }, |
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doi = { 10.57967/hf/3678 }, |
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publisher = { Hugging Face } |
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} |
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## Model Card Authors |
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- Keyvan Hardani |
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## Contact |
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For questions or support, please reach out via [GitHub Issues](https://github.com/Keyvanhardani/Anomaly-Detection-Metal/issues) |
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