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