| --- |
| license: apache-2.0 |
| datasets: |
| - mvtec-ad |
| metrics: |
| - auroc |
| - f1 |
| pipeline_tag: image-segmentation |
| tags: |
| - anomaly-detection |
| - industrial-inspection |
| - mvtec-ad |
| - deep-learning |
| - openvino |
| - quality-control |
| library_name: openvino |
| --- |
| |
| # 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. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| - **Developed by:** Keyvan Hardani |
| - **Shared by:** [GitHub Repository](https://github.com/Keyvanhardani/Anomaly-Detection-Metal) |
| - **Model type:** Image segmentation and anomaly detection |
| - **License:** Apache 2.0 |
| - **Finetuned from model:** None |
|
|
| ### Model Sources |
|
|
| - **Repository:** [GitHub Link](https://github.com/Keyvanhardani/Anomaly-Detection-Metal) |
| - **Demo:** [Hugging Face Demo Link](https://huggingface.co/spaces) |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## 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. |
|
|
| --- |
|
|
| ## 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](https://github.com/Keyvanhardani/Anomaly-Detection-Metal). |
|
|
| --- |
|
|
| ## 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 |
|
|
| --- |
|
|
| ## 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 |
|
|
| --- |
|
|
| ## Environmental Impact |
|
|
| - **Hardware Type:** GPU-based training and inference (NVIDIA RTX 4080) |
| - **Hours used:** Approx. 10 hours |
| - **Carbon Emitted:** [Estimate pending] |
|
|
| --- |
|
|
| ## 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 } |
| } |
| |
| --- |
|
|
| ## Model Card Authors |
|
|
| - Keyvan Hardani |
|
|
| ## Contact |
|
|
| For questions or support, please reach out via [GitHub Issues](https://github.com/Keyvanhardani/Anomaly-Detection-Metal/issues) |
|
|
