---
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)