Call Output

Deep Learning > Call Output

Calls an output from a Deep Learning model applied earlier in the recipe. This is useful for performing separate processing workflows on different probability maps from a single Apply Model step.

This step works with both segmentation models (SegNet/U-Net) and YOLO models.

1. Output

Choose image to display and output.

For Segmentation Models (SegNet/U-Net):

• Probability Maps: One for each of the trained layers. A probability map indicates each pixel’s likelihood to belong to that layer.
• Layer Map: Represents the most likely layer classification for each pixel.

For YOLO Models:

• Detections for Single Layer: One for each of the trained layers. Bounding boxes shown with confidence score for each detection above the Confidence Threshold for the selected layer.
• All Features: Bounding boxes shown with layer labels for each detection above the Confidence Threshold for all layers.

*The final output for YOLO models are grayscale images for compatibility with existing recipe workflows. For “single layer” outputs, box intensities reflect their confidence score. For “all features” outputs, box intensities reflect their class. Use a Basic Threshold following the output to select features.