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HOW_to_Use_KV260_Deploy_YOLO_application

介绍

使用KV260板部署YOLO应用的实践过程

KV260部署一个yolo的过程.md

介绍

目前KV260 AI-model支持

  • IVS_XCLASS_YOLOV3
  • IVS_XCLASS_FACEDETECT
  • IVS_XCLASS_CLASSFICATION
  • IVS_XCLASS_SSD
  • IVS_XCLASS_REID
  • IVS_XCLASS_REFINEDET
  • IVS_XCLASS_TFSSD
  • IVS_XCLASS_YOLOV2
  • IVS_XCLASS_NOTFOUND

我的配置

VMware虚拟机 ,Docker Image Version:  1.4.1.978 ,Vitis AI Git Hash:  9f3d6db ,Ubuntu 18.04.6 LTS 

准备一个模型

AI软件工程师负责

    1. 准备一个训练好的模型 - 这个步骤是普通的AI模型
    • 注意事项
      • 1: 如果自己训练需要保证环境兼容,已知Vitis AI DOCKER在CUDA11下是有问题的。 可以不用在docker里做编译
      • 2: 模型的算子需要被DPU支持 查一下B站文章
      • 3: DK模型不能直接使用,它需要被转换,具体转换参考Vitis官方手册和教程
    1. 训练好模型之后,我们需要去剪枝和量化模型
    • 注意事项
      • 1: 对于KV260而言,30G左右的模型是能够流畅运行的极限了,所以模型剪枝的越小,系统就越流 畅。如果不剪枝的话,对于YOLO模型来说,就会很慢。
    1. 需要使用Vitis AI的工具去量化模型
    • 注意事项
      • 1: Vitis的量化器,效果并不是特别好,可以自己量化一遍再给Vitis量化器来使用。

从量化好的模型转换到Xmodel的文件

  • 重要参考工具文献: 1.4 UG1354 Vitis AI Library User Guide
  • 包含介绍、安装、库和例子、编程实例
  • 注意事项:
    • 不同版本的代码又可能不兼容
  • AI library API的框图 ![AI library API] (C:\kv260_SoM\KV260_YOLO\AI library API.png)
  1. 对于KV260的example而言,我们使用的是API_1, API_1一般情况下,不知需要Xmodel,还需要其他文件,一般情况是需要protext 和 label.json 。其中,有些文件需要用户自己生成。所以我们在部署YOLO时,也要准备对应文件。

  2. 可以从Model Zoo获得对应的量化后的模型。

  3. 量化后的模型需要在Vitis AI docker中进行编译。

/opt/Xilinx/share/vitis_ai_library/models/kv260-smartcam/yolov2-voc/ --yolov2_voc.xmodel --yolov2_voc.prototxt --label.json /opt/Xilinx/share/ivas/smartcam/new_yolov3 --preprocess.json --drawresult.json --aiinference.json

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