{"id":31815815,"url":"https://github.com/xilinx/vitis-ai-tutorials","last_synced_at":"2025-10-11T09:24:57.006Z","repository":{"id":37632879,"uuid":"244495264","full_name":"Xilinx/Vitis-AI-Tutorials","owner":"Xilinx","description":null,"archived":false,"fork":false,"pushed_at":"2024-06-12T18:40:27.000Z","size":1717419,"stargazers_count":430,"open_issues_count":67,"forks_count":150,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-03-20T10:39:07.108Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Xilinx.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-03-02T23:10:46.000Z","updated_at":"2025-03-19T03:06:24.000Z","dependencies_parsed_at":"2024-01-16T02:45:33.471Z","dependency_job_id":"f94211a9-5022-466b-9826-4d7812383775","html_url":"https://github.com/Xilinx/Vitis-AI-Tutorials","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Xilinx/Vitis-AI-Tutorials","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xilinx%2FVitis-AI-Tutorials","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xilinx%2FVitis-AI-Tutorials/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xilinx%2FVitis-AI-Tutorials/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xilinx%2FVitis-AI-Tutorials/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Xilinx","download_url":"https://codeload.github.com/Xilinx/Vitis-AI-Tutorials/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Xilinx%2FVitis-AI-Tutorials/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279006755,"owners_count":26084178,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-11T02:00:06.511Z","response_time":55,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-10-11T09:24:56.199Z","updated_at":"2025-10-11T09:24:57.001Z","avatar_url":"https://github.com/Xilinx.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ctable class=\"sphinxhide\"\u003e\r\n \u003ctr width=\"100%\"\u003e\r\n    \u003ctd align=\"center\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/Xilinx/Image-Collateral/main/xilinx-logo.png\" width=\"30%\"/\u003e\u003ch1\u003eVitis-AI™ Tutorials\u003c/h1\u003e\r\n    \u003ca href=\"https://www.xilinx.com/products/design-tools/vitis.html\"\u003eSee Vitis™ Development Environment on xilinx.com\u003c/br\u003e\u003c/a\u003e\r\n    \u003ca href=\"https://www.xilinx.com/products/design-tools/vitis/vitis-ai.html\"\u003eSee Vitis-AI™ Development Environment on xilinx.com\u003c/a\u003e\r\n    \u003c/td\u003e\r\n \u003c/tr\u003e\r\n\u003c/table\u003e\r\n\r\n\u003ctable\u003e\r\n\u003cthead\u003e\r\n  \u003ctr\u003e\r\n    \u003cth width=\"35%\" align=\"center\"\u003e\u003ch3\u003e\u003cb\u003eTutorial Name\u003c/b\u003e\u003c/hr\u003e\u003c/th\u003e\r\n    \u003cth width=\"15%\" align=\"center\"\u003e\u003ch3\u003e\u003cb\u003eLatest Supported Vitis AI Version\u003c/b\u003e\u003c/hr\u003e\u003c/th\u003e\r\n    \u003cth width=\"50%\" align=\"center\"\u003e\u003ch3\u003e\u003cb\u003eDescription\u003c/b\u003e\u003c/hr\u003e\u003c/th\u003e\r\n  \u003c/tr\u003e\r\n\u003c/thead\u003e\r\n\u003ctbody\u003e\r\n  \u003ctr\u003e\r\n  \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.5/Tutorials/RESNET18/\"\u003eRunning ResNet18 CNN Through Vitis AI 3.5 Flow for ML\u003c/a\u003e\r\n \u003c/td\u003e\r\n \u003ctd align=\"center\"\u003e3.5\u003c/td\u003e\r\n \u003ctd\u003eIn this Deep Learning (DL) tutorial, you will take a public domain CNN like ResNet18, already trained on the ImageNet dataset, and run it through the Vitis AI 3.5 stack to run ML inference on FPGA devices. You will use Keras on Tensorflow 2.x. Supported boards are: ZCU104, ZCU102, VCK190, VEK280 and Alveo V70.\r\n\u003c/td\u003e\r\n \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n  \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.5/Tutorials/PyTorch-ResNet18/\"\u003eResNet18 in PyTorch from Vitis AI Library\u003c/a\u003e\r\n \u003c/td\u003e\r\n \u003ctd align=\"center\"\u003e3.5\u003c/td\u003e\r\n \u003ctd\u003eIn this Deep Learning (DL) tutorial, you will take the ResNet18 CNN, from the Vitis AI 3.5 PyTorch Library, and use it to classify the different colors of the \"car object\" inside images by running the inference application on FPGA devices. Supported boards are: ZCU104, ZCU102, VCK190, VEK280 and Alveo V70.\r\n\u003c/td\u003e\r\n \u003c/tr\u003e\r\n \u003ctr\u003e\r\n  \u003ctd\u003e\r\n   \u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.5/Tutorials/TF2-Vitis-AI-Optimizer/\"\u003eTensorFlow2 Vitis AI Optimizer: Getting Started\u003c/a\u003e\r\n \u003c/td\u003e\r\n \u003ctd align=\"center\"\u003e3.5\u003c/td\u003e\r\n \u003ctd\u003eGet started with the \u003ca href=\"https://docs.xilinx.com/r/en-US/ug1414-vitis-ai/Vitis-AI-Optimizer\"\u003eVitis AI Optimizer (release 3.5)\u003c/a\u003e in the TensorFlow2 (TF2) environment with Keras.\u003c/td\u003e\r\n \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n  \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.0/Tutorials/Keras_GoogleNet_ResNet/\"\u003eDeep Learning with Custom GoogleNet and ResNet in Keras and Xilinx Vitis AI\u003c/a\u003e\u003c/td\u003e\r\n \u003ctd align=\"center\"\u003e3.0\u003c/td\u003e\r\n \u003ctd\u003eQuantize 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).\u003c/td\u003e\r\n \u003c/tr\u003e\r\n \u003ctr\u003e\r\n \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.0/Tutorials/pytorch-subgraphs/\"\u003ePartitioning Vitis AI SubGraphs on CPU/DPU\u003c/a\u003e\u003c/td\u003e\r\n \u003ctd align=\"center\"\u003e3.0\u003c/td\u003e\r\n \u003ctd\u003eLearn how to deploy a CNN on the Xilinx \u003ca href=\"https://www.xilinx.com/products/boards-and-kits/vck190.html\"\u003eVCK190\u003c/a\u003e board using Vitis AI.\u003c/td\u003e\r\n \u003c/tr\u003e\r\n \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.0/Tutorials/Keras_FCN8_UNET_segmentation\"\u003eFCN8 and UNET Semantic Segmentation with Keras and Xilinx Vitis AI\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e3.0\u003c/td\u003e\r\n    \u003ctd\u003eTrain 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.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n \u003ctr\u003e\r\n  \u003ctd\u003e\r\n   \u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/3.0/Tutorials/18-mpsocdpu-pre-post-pl-acc/\"\u003ePre- and Post-processing Accelerators for Semantic Segmentation with Unet CNN on MPSoC DPU\u003c/a\u003e\r\n \u003c/td\u003e\r\n \u003ctd align=\"center\"\u003e3.0\u003c/td\u003e\r\n \u003ctd\u003eA complete example of how using the \u003ca href=\"https://github.com/Xilinx/Vitis-AI/tree/3.0/demo/Whole-App-Acceleration\"\u003eWAA\u003c/a\u003e flow targeting the MPSoC ZCU102 board.\r\n\u003c/td\u003e\r\n \u003c/tr\u003e\r\n \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.5/Tutorials/Kaggle_ImageNet/\"\u003eUsing the Kaggle ImageNet Subset for Training Neural Networks\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.5\u003c/td\u003e\r\n    \u003ctd\u003eDemonstrates how to use the Kaggle ImageNet Subset for training neural networks for developers and enthusiasts with a non-edu domain who are unable to obtain the ImageNet dataset directly.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n   \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.5/Tutorials/RFModulation_Recognition/\"\u003eRF Modulation Recognition with Vitis AI\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.5\u003c/td\u003e\r\n    \u003ctd\u003eDiscusses using Deep Neural Networks to perform automatic modulation recognition so that the receiver may be able to detect and demodulate the signal without this explicit knowledge of the modulation type and encoding method.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.0/Tutorials/Vitis-AI-Vivado-TRD/README.md\"\u003eLeveraging the Vitis™ AI DPU in the Vivado® Workflow\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.0\u003c/td\u003e\r\n    \u003ctd\u003eBuild the Vitis AI Targeted Reference Design (TRD) using the Vivado flow and learn how to build a PetaLinux image from the ZCU102 BSP that is provided in the TRD archive.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.0/Tutorials/caffe_cats_vs_dogs/README.md\"\u003eQuantization and Pruning of AlexNet CNN trained in Caffe with Cats-vs-Dogs dataset\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.0\u003c/td\u003e\r\n    \u003ctd\u003eTrain, 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.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.0/Tutorials/Vitis-AI-on-VCK5000-ES-Board/\"\u003eVitis AI on VCK5000 Card\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.0\u003c/td\u003e\r\n    \u003ctd\u003eStart from card installation and go through a step-by-step workflow to run the first Vitis AI sample on a VCK5000 card.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.0/Tutorials/VCK190_CUSTOM_LAMBDA_OP/\"\u003eVCK190 Custom Lambda Operator\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.0\u003c/td\u003e\r\n    \u003ctd\u003eThe general concept behind the custom operator flow is to make Vitis AI and the DPU more extensible—both for supporting custom layers as well as framework layers that are currently unsupported in the toolchain. The custom operator flow enables you to define layers which are unsupported, and ultimately deploy those layers either on the CPU or an accelerator.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/2.0/Tutorials/kv260_lidar_cam_fusion/\"\u003eLIDAR + Camera Fusion on KV260\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e2.0\u003c/td\u003e\r\n    \u003ctd\u003eShows you how to install Ubuntu on the KV260 then build ROS, bring in multiple sensors, and deploy FPGA-accelerated neural network to process the data before displaying the data using RViz. All of this is possible without ever using FPGA tools!\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Introduction/README.md\"\u003eIntroduction to Vitis AI\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eThis tutorial puts in practice the concepts of FPGA acceleration of Machine Learning and illustrates how to\u003cbr\u003e quickly get started deploying both pre-optimized and customized ML models on Xilinx devices.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/02-MNIST_classification_tf/README.md\"\u003eMNIST Classification using Vitis AI and TensorFlow\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eLearn 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.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/03-using_densenetx/README.md\"\u003eUsing DenseNetX on the Xilinx DPU Accelerator\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eLearn 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.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n   \r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.3/Design_Tutorials/06-densenetx_DPUv3\"\u003eUsing DenseNetX on the Xilinx Alveo U50 Accelerator Card\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.3\u003c/td\u003e\r\n    \u003ctd\u003eImplement a convolutional neural network (CNN) and run it on the DPUv3E accelerator IP.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/07-yolov4-tutorial/readme.md\"\u003eVitis AI YOLOv4\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eLearn how to train, evaluate, convert, quantize, compile, and deploy YOLOv4 on Xilinx devices using Vitis AI.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/08-tf2_flow/README.md\"\u003eTensorFlow2 and Vitis AI design flow\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eLearn about the TF2 flow for Vitis AI. In this tutorial, you'll be trained on TF2, including conversion of a dataset into TFRecords, optimization with a plug-in, and compiling and execution on a Xilinx ZCU102 board or Xilinx Alveo U50 Data Center Accelerator card.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/09-mnist_pyt/README.md\"\u003ePyTorch flow for Vitis AI\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eIntroduces the Vitis AI TensorFlow design process and illustrates how to go from a python description of the network model to running a compiled model on a Xilinx evaluation board.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/10-RF_modulation_recognition/README.md\"\u003eRF Modulation Recognition with TensorFlow 2\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eMachine learning applications are certainly not limited to image processing! Learn how to apply machine learning with Vitis AI to the recognition of RF modulation from signal data.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/11-tf2_var_autoenc/README.md\"\u003eDenoising Variational Autoencoder with TensorFlow2 and Vitis-AI\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eThe Xilinx DPU can accelerate the execution of many different types of operations and layers that are commonly found in convolutional neural networks but occasionally we need to execute models that have fully custom layers. One such layer is the sampling function of a convolutional variational autoencoder. The DPU can accelerate the convolutional encoder and decoder but not the statistical sampling layer - this must be executed in software on a CPU. This tutorial will use the variational autoencoder as an example of how to approach this situation.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/12-Alveo-U250-TF2-Classification/README.md\"\u003eAlveo U250 TF2 Classification\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eDemonstrates image classification using the Alveo U250 card with Vitis AI 1.4 and the Tensorflow 2.x framework.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/13-vdpu-pre-post-pl-acc/README.md\"\u003ePre- and Post-processing PL Accelerators for ML with Versal DPU\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eA complete example of how using the \u003ca href=\"https://github.com/Xilinx/Vitis-AI/tree/master/demo/Whole-App-Acceleration\"\u003eWAA\u003c/a\u003e flow with Vitis 2020.2 targeting the VCK190 PP board.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/14-caffe-ssd-pascal/README.md\"\u003eCaffe SSD\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eThe topics covered in this tutorial include training, quantizing, and compiling SSD using PASCAL VOC 2007/2012 datasets, the Caffe framework, and Vitis AI tools. The model is then deployed on a Xilinx® ZCU102 target board and could also be deployed on other Xilinx development board targets (For example, Kria Starter Kit, ZCU104, and VCK190).\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/15-caffe-segmentation-cityscapes/README.md\"\u003eML Caffe Segmentation\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eDescribes how to train, quantize, compile, and deploy various segmentation networks using Vitis AI, including ENet, ESPNet, FPN, UNet, and a reduced compute version of UNet that we'll call Unet-lite. The training dataset used for this tutorial is the Cityscapes dataset, and the Caffe framework is used for training the models.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/16-profiler_introduction/README.md\"\u003eIntroduction Tutorial to the Vitis AI Profiler\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eIntroduces the the Vitis AI Profiler tool flow and will illustrates how to profile an example from the Vitis AI runtime (VART).\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Design_Tutorials/17-PyTorch-CityScapes-Pruning/README.md\"\u003ePyTorch CityScapes Pruning\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eThe following is a tutorial for using the Vitis AI Optimizer to prune the Vitis AI Model Zoo FPN Resnet18 segmentation model and a publicly available UNet model against a reduced class version of the Cityscapes dataset. The tutorial aims to provide a starting point and demonstration of the PyTorch pruning capabilities for the segmentation models.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n     \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Feature_Tutorials/tf2_quant_fine_tune/README.md\"\u003eFine-Tuning TensorFlow2 quantized model\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eLearn how to implement the Vitis-AI quantization fine-tuning for TensorFlow2.3.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Feature_Tutorials/Vitis-AI-based-Deployment-Flow-on-VCK190/README.md\"\u003eVitis AI based Deployment Flow on VCK190\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eDPU integration with VCK190 production platform.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.4/Feature_Tutorials/04-tensorflow-ai-optimizer/README.md\"\u003eTensorFlow AI Optimizer Example Using Low-level Coding Style\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.4\u003c/td\u003e\r\n    \u003ctd\u003eUse AI Optimizer for TensorFlow to prune an AlexNet CNN by 80% while maintaining the original accuracy.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.3/Feature_Tutorials/01-freezing_a_keras_model\"\u003eFreezing a Keras Model for use with Vitis AI (UG1380)\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.3\u003c/td\u003e\r\n    \u003ctd\u003eFreeze a Keras model by generating a binary protobuf (.pb) file.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.3/Feature_Tutorials/02-profiling-example\"\u003eProfiling a CNN Using DNNDK or VART with Vitis AI (UG1487)\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.3\u003c/td\u003e\r\n    \u003ctd\u003eProfile a CNN application running on the ZCU102 target board with Vitis AI.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n  \u003ctr\u003e\r\n    \u003ctd\u003e\u003ca href=\"https://github.com/Xilinx/Vitis-AI-Tutorials/tree/1.3/Feature_Tutorials/03-edge-to-cloud\"\u003eMoving Seamlessly between Edge and Cloud with Vitis AI (UG1488)\u003c/a\u003e\u003c/td\u003e\r\n    \u003ctd align=\"center\"\u003e1.3\u003c/td\u003e\r\n    \u003ctd\u003eCompile 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.\u003c/td\u003e\r\n  \u003c/tr\u003e\r\n\u003c/tbody\u003e\r\n\u003c/table\u003e\r\n\r\n\r\n\r\n\u003c/hr\u003e\r\n\r\n\r\n\u003cp class=\"sphinxhide\" align=\"center\"\u003e\u003csub\u003eCopyright © 2022–2023 Advanced Micro Devices, Inc\u003c/sub\u003e\u003c/p\u003e\r\n\r\n\u003cp class=\"sphinxhide\" align=\"center\"\u003e\u003csup\u003e\u003ca href=\"https://www.amd.com/en/corporate/copyright\"\u003eTerms and Conditions\u003c/a\u003e\u003c/sup\u003e\u003c/p\u003e\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxilinx%2Fvitis-ai-tutorials","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fxilinx%2Fvitis-ai-tutorials","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fxilinx%2Fvitis-ai-tutorials/lists"}