You can have many different application requirements, but all of them can be categorized into three categories:
- Using the models that are provided by the Vitis™ AI Library to quickly build your own application.
- Using your own custom models which are similar to the models in the Vitis AI Library.
- Using new models that are totally different from the models in the Vitis AI Library.
This chapter describes the detailed development steps for the first two cases. For the third case, you can use the Vitis AI Library samples and implementation for reference. This chapter provides information on:
- Customizing preprocessing
- Using the configuration file as preprocessing and postprocessing parameter
- Using the Vitis AI Library's postprocessing library
- Implementing user postprocessing code
- Working with the xdputil tool
The following figure shows the relationship between the Vitis AI Library APIs and their corresponding examples. There are four kinds of APIs in this release:
- Vitis AI Library API_0 based on VART
- Vitis AI Library API_1 based on AI Library
- Vitis AI Library API_2 based on DpuTask
- Vitis AI Library API_3 based on Graph_runner
Figure 1. The Diagram of AI Library API
