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| Tutorial | Description |
| Quantization and Pruning of AlexNet CNN trained in Caffe with Cats-vs-Dogs dataset (UG1336) | Train, prune, and quantize a modified version of the AlexNet convolutional neural network (CNN) with the Kaggle Dogs vs. Cats dataset in order to deploy it on the Xilinx® ZCU102 board. |
| MNIST Classification using Vitis™ AI and TensorFlow (UG1337) | Learn the Vitis AI TensorFlow design process for creating a compiled ELF file that is ready for deployment on the Xilinx DPU accelerator from a simple network model built using Python. This tutorial uses the MNIST test dataset. |
| CIFAR10 Classification using Vitis AI and TensorFlow (UG1338) | Learn the Vitis AI TensorFlow design process for creating a compiled ELF file that is ready for deployment on the Xilinx DPU accelerator from a simple network model built using Python. This tutorial uses the CIFAR-10 test dataset. |
| Using DenseNetX on the Xilinx DPU Accelerator (UG1340) | Learn about the Vitis AI TensorFlow design process and how to go from a Python description of the network model to running a compiled model on the Xilinx DPU accelerator. |
| Freezing a Keras Model for use with Vitis AI (UG1380) | Freeze a Keras model by generating a binary protobuf (.pb) file. |
| Deep Learning with Custom GoogleNet and ResNet in Keras and Xilinx Vitis AI (UG1381) | Quantize in fixed point some custom CNNs and deploy them on the Xilinx ZCU102 board, using Keras and the Xilinx7Vitis AI tool chain based on TensorFlow (TF). |
| ML Caffe Segmentation Tutorial (UG1394) | Use Vitis AI to train, quantize, compile, and deploy various segmentation networks including: ENet, ESPNet, FPN, UNet, and a reduced compute version of UNet. |
| FCN8 and UNET Semantic Segmentation with Keras and Xilinx Vitis AI (UG1445) | Train the FCN8 and UNET Convolutional Neural Networks (CNNs) for Semantic Segmentation in Keras adopting a small custom dataset, quantize the floating point weights files to an 8-bit fixed point representation, and then deploy them on the Xilinx® ZCU102 board using Vitis AI. |
| ML at the Edge - Introduction Lab (UG1447) | Use the Vitis AI 1.0 tool kit to quantize and compile a Yolov3 TensorFlow model that utilizes the Xilinx Deep Learning Processor (DPU) on the ZCU104 board. The Yolov3 model was trained on the Pascal VOC data set. |
| Vitis AI (on Ultra96V2) Custom Platform Tutorials (UG1454) | A set of tutorials related to porting Vitis AI 1.0 to a custom platform. |
| ML SSD PASCAL Caffe Tutorial (UG1457) | Train, quantize, and compile SSD using PASCAL VOC 2007/2012 datasets, the Caffe framework, and Vitis AI tools. Then deploy the model on a Xilinx ZCU102 target board. |
| Medical AI Application Acceleration with Xilinx AI Stack (UG1467) | Take a medical dataset, develop and evaluate an end-to-end AI accelerated application using Xilinx Vitis AI, without writing any lower level RTL code. |
| Profiling a CNN Using DNNDK or VART with Vitis AI (UG1487) | Profile a CNN application running on the ZCU102 target board with Vitis AI. |
| Moving Seamlessly between Edge and Cloud with Vitis AI (UG1488) | Compile and run the same identical design and application code on either the Alveo U50 data center accelerator card or the Zynq UltraScale+™ MPSoC ZCU102 evaluation board. |
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