You can have many different application requirements, but all of them can be categorized into three categories. The first is to use the ready-made models provided by the Xilinx® Vitis™ AI library to quickly build their own application; the second is to use your own custom models which are similar to the models in the Vitis AI Library; and the last is to use 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 also use the Vitis AI library samples and libraries implementation for reference. Therefore, this chapter describes the following contents:
- Customizing pre-processing
- Using the configuration file as pre-processing and post-processing parameter
- Using the Vitis AI Library's post-processing library
- Implementing user post-processing code
- Working with the xdputil tool
The following figure shows the relationships of the various Vitis AI library APIs and their corresponding example. And 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
