You need to agree to share your contact information to access this model

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Log in or Sign Up to review the conditions and access this model content.

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


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.

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

Downloads last month

-

Downloads are not tracked for this model. How to track
Inference Examples
Inference API (serverless) does not yet support openvino models for this pipeline type.