Once an initial inference has been run with a model sample dataset and target, you can view performance results on the project dashboard.
The components specified below provide visual representation of a model performance on a selected dataset and help find potential bottlenecks and areas for improvement:
The Model Analyzer is used for generating estimated performance information on neural networks. The tool analyzes of the following characteristics:
|Characteristic||Unit of Measurement||Explanation|
|Computational Complexity||GFLOPs||Represents a number of floating point operations required to infer a model.|
|Number of Parameters||Millions||Represents a total number of weights in a model.|
|Minimum Memory Consumption,|
Maximum Memory Consumption
|Millions of units||A unit depends on the precision of model weights. For example, for FP32 model these parameters must be multiplied by 4 bytes.|
Model analysis data is collected when the model is imported. All parameters depend on the size of a batch. Currently, information is gathered on the default model batch.
To view model analysis data, click the plus button next to a model name on the Configurations page.
A table with characteristics of a model appears: