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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)
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