Edge AI Tutorials

Zynq 7000 DPU TRD

dnndk3.0-pynqz2

• In this tutorial you will learn:

  1. How to use caffe model resnet50 to classify pictures using pynq-z2.
  2. How to use tensorflow model mnist to recognize hand-writing number using pynq-z2.
  3. How to train and use yolov3 in pynq-z2.
  4. How to use DNNDK-v3.0 to optimize the trained models.
  5. How to use dpu in pynq-z2 to accelerate inference.

• First download all the files to your pc.

  You can also download the system image of pynq-z2 we provided here, it embeds DPU IP into pynq system and fixes some problems of official image. For more details, please refer to pynq_car.

• The most important files are organized as followed:

  mnist_tf

  mnist_host
  mnist_pynqz2
  mnist-handwriting-guide.md

  resnet50_caffe

  resnet50_host
  resnet50_pynqz2
  resnet50_pynqz2_guide.md

  yolo_keras

  keras-yolo3
  yolo_pynqz2
  take_training_imgs
  yolo_pynqz2_guide.md

  The mnist_tf contains the mnist model trained by tensorflow and you can read the mnist-handwriting-guide.md to learn. The resnet50_caffe contains the resnet50 model trained by caffe and you can read the resnet50_pynqz2_guide.md to learn. The yolo_keras provide a yolo implementation using keras, you can download the pre-trained weights of yolo from darknet.

• Preparation

  Before you start, you should read build-host-dnndk.md & build-pynqz2-system.md first to set your environment and do some preparation. I recommend you learn mnist_tf before running into yolo_keras.

If you have any problem, please open an issue. If you like this project please star it to support.