Input tensor (Inference Overlay)

An Input tensor is a multi-dimensional data array that serves as input for neural networks. Generally these input tensors are filled with (parts of) one or more images, of a given width and height and with one more more color channels. These images will be processed by the neural network to classify the image or detect features in the image.

In the Tygron Platform, also Grid Overlays, often Satellite Overlays or WMS Overlays, can serve as input for neural networks. How an input tensor of a neural network is filled by an Inference Overlay is configured using Tensor Links.

A tensor link references:

• The input tensor, identified by the n (images) and c (channel) tuple, which are mentioned in the name of the tensor link.
• The prequel of an Inference Overlay that should be used to obtain values from
• What the data of the prequel represents;
  • When it is a color, you can specify which color channel should be used
  • When it is a floating point value, simple specify DEFAULT.
• Whether the value should be normalized; Color channels are always normalized between 0 and 255. Floating point values are normalized using the calculated min- and max-value of the specified input prequel.

Notes

• The inference overlay wizard will report an issue when a prequel is referenced by a tensor link and it is not yet specified for the Inference Overlay.

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Inference Overlay

Inference Overlay

Tutorials

Overview

Theory

Benchmarks

Group

Model attributes

Inference

Neural Network • Input tensor • Output tensor

Model attributes

Bbox overlap • Inference mode • Mask threshold • Score threshold • Stride fraction

Prequels

A • B

Result types

Boxes • Labels • Masks • Scores • Debug gt instances

Neural Networks

Foliage • Solar panels