# DL - Predict Object Class

This is an experimental module from the Xtra Library: https://www.thermofisher.com/software-em-3d-vis/xtra-library/.

The DL Prediction Object Classifier applies trained deep learning models to classify objects in 2D or 3D images.

The module extracts patches around each object instance, applies the trained model and generates a classified label field, an instance label field (if it was not provided as input) and a spreadsheet data containing predicted classes and confidence scores.

Input models need to be trained from the DL 2D - Train Object Classifier or DL 3D - Train Object Classifier modules.

## Connections

Input | Description
--- | ---
Data [required] | Binary or label field containing object instances to be classified.
Input Grayscale Volume [required] | Grayscale image or volume from which object patches will be extracted for classification.

## Parameters (Ports)

Parameter | Description
--- | ---
Console | Displays status messages and technical information.
Model Filename | Path to the trained model file (.pth). The module automatically detects whether the model is 2D or 3D.
Model Information | Displays loaded model details including architecture, number of classes, input size, and pixel/voxel size.
Batch Size | Number of patches processed simultaneously. Larger values speed up prediction but require more memory.
Background correction | Enable to subtract background intensity variations from input grayscale volume. Useful for images with uneven illumination.
Background correction parameters | Number of grid points used to approximate background intensities. See Background Detection Correction Module (BSpline Type).

## See also

DL 2D - Train Object Classifier

DL 3D - Train Object Classifier

ObjectClassification_Tuto.pdf