Input tensor (Inference Overlay)
An Input tensor is a multi-dimensional data array that serves as input for RCNNs. These input tensors generally contain one or more images (or parts of them) with a specific width, height, and number of 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's width, height, and its n (images) and c (channel) tuple specified in the tensor link name.
- The prequel of an Inference Overlay that should be used to obtain values from
- The value type of the data of the prequel;
- When it is a color, you can specify which color channel should be used; Red, Green, Blue or Alpha
- When it is a floating point value, simply 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 overlay.
Since the input tensor has a limited width and height, the Inference Overlay model uses the algorithm that moves an input window of this width and height over the prequel grid. Since features can be situated on the edges, the step by which the input tensor is moved can be configured using the Stride fraction model attribute.
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.