inf_data_preprocessing.qmd

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title: Data Preprocessing for Inferencing
order: 4
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# Data Preprocessing for Inferencing

Inference is decomposed into three distinct steps, and this is the first: **data preprocessing for inferencing**. The `inf_data_preprocessing` pipeline includes nodes for:

1. **Input Image Preprocessing**  
2. **Out-of-Distribution (OOD) Detection**  

You can extend this pipeline by adding nodes to perform additional operations on input images before they are passed to the model.

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## OOD Detection

For **Out-of-Distribution (OOD) Detection**, we use the `pytorch-ood` library. A separate pipeline, titled `ood_detection`, is provided to train and integrate custom OOD detectors.

### Features of OOD Detection Pipeline

- **Preconfigured Templates**  
  We provide templates for the following OOD detectors:
  - **MSP Detector**: Maximum Softmax Probability Detector  
  - **RMD Detector**: Relative Mahalanobis Distance Detector  
  - **Multi-Mahalanobis Detector**: An enhanced version of the Mahalanobis-based detector.

- **Dataset Example**  
  The pipeline is configured to use CIFAR-10 as the "out-of-distribution" dataset by default. However, you can adapt the pipeline to use any dataset of your choice.

### Getting Started with OOD Detection

1. Train your custom OOD detector by modifying the templates provided in the `ood_detection` pipeline.
2. For further customization or advanced features, refer to the [PyTorch OOD Documentation](https://pytorch-ood.readthedocs.io/en/v0.2.0/info.html).

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## Customization Tips

- **Add Preprocessing Steps**: Extend the `inf_data_preprocessing` pipeline to include additional transformations or preprocessing steps suited to your use case.
- **Experiment with OOD Detectors**: Customize the OOD pipeline to use different datasets or fine-tune the provided detectors to improve performance.