diff --git a/docs/.buildinfo b/docs/.buildinfo index a69c9df06..1bc650a3e 100644 --- a/docs/.buildinfo +++ b/docs/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 69378c3e4e70920506b6c832d7ea1ffb +config: e8809926e287b8b0d9656262fa0b07bc tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_downloads/ff9554ff9ff6240811c20ede15113dbd/ModelZoo_Github.xlsx b/docs/_downloads/ff9554ff9ff6240811c20ede15113dbd/ModelZoo_Github.xlsx index c35113101..9a9be80f7 100644 Binary files a/docs/_downloads/ff9554ff9ff6240811c20ede15113dbd/ModelZoo_Github.xlsx and b/docs/_downloads/ff9554ff9ff6240811c20ede15113dbd/ModelZoo_Github.xlsx differ diff --git a/docs/_images/VEK280_Top_img.png b/docs/_images/VEK280_Top_img.png new file mode 100644 index 000000000..acc168dbf Binary files /dev/null and b/docs/_images/VEK280_Top_img.png differ diff --git a/docs/_sources/docs/install/China_Ubuntu_servers.rst.txt b/docs/_sources/docs/install/China_Ubuntu_servers.rst.txt index f6ac2772d..bdd10aa45 100644 --- a/docs/_sources/docs/install/China_Ubuntu_servers.rst.txt +++ b/docs/_sources/docs/install/China_Ubuntu_servers.rst.txt @@ -9,7 +9,7 @@ Vitis |trade| AI Docker images leverage Ubuntu 20.04. In your Ubuntu installatio deb http://us.archive.ubuntu.com/ubuntu/ focal universe -You can see that the hostname “archive.ubuntu.com” resolves to servers located within the United States. When building the Vitis AI Docker image, whether for `CPU-only `__ or `GPU `__ applications Docker will attempt to pull from US servers. As a result, users accessing from China will generally experience slow download speeds. +You can see that the hostname “archive.ubuntu.com” resolves to servers located within the United States. When building the Vitis AI Docker image, whether for CPU-only or GPU accelerated containers Docker will attempt to pull from US servers. As a result, users accessing from China will generally experience slow download speeds. Prior to building the Vitis AI Docker image it is recommended that you modify **/etc/apt/sources.list** and the vitis-ai-gpu.Dockerfile. diff --git a/docs/_sources/docs/quickstart/v70.rst.txt b/docs/_sources/docs/quickstart/v70.rst.txt index 251f90f2d..38fe1bb49 100644 --- a/docs/_sources/docs/quickstart/v70.rst.txt +++ b/docs/_sources/docs/quickstart/v70.rst.txt @@ -2,7 +2,7 @@ Quick Start Guide for Alveo V70 ############################### -The AMD **DPUCV2DX8G** for the Alveo |trade| V70 is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support V70. +The AMD **DPUCV2DX8G** for the Alveo |trade| V70 is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you install the software and packages required to support V70. .. image:: ../reference/images/V70.PNG :width: 1300 @@ -114,7 +114,7 @@ From inside the docker container, execute one of the following commands to set t Vitis-AI Model Zoo ================== -You can now select a model from the Vitis AI Model Zoo `Vitis AI Model Zoo <../workflow-model-zoo.html>`__. Navigate to the `model-list subdirectory `__ and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target. Choose the correct link for your target platform and download the model. +You can now select a model from the `Vitis AI Model Zoo <../workflow-model-zoo.html>`__. Navigate to the `model-list subdirectory `__ and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target. Choose the correct link for your target platform and download the model. - Take the ResNet50 model as an example. @@ -144,7 +144,7 @@ Run the Vitis AI Examples .. code-block:: Bash - [Docker] $ tar -xzvf vitis_ai_runtime_r3.5.0_image_video.tar.gz -C /w/examples/vai_runtime + [Docker] $ tar -xzvf vitis_ai_runtime_r3.5.0_image_video.tar.gz -C /workspace/examples/vai_runtime 3. Navigate to the example directory. @@ -157,6 +157,7 @@ Run the Vitis AI Examples .. code-block:: Bash + [Docker] $ sudo chmod u+r+x build.sh [Docker] $ bash -x build.sh 5. Run the example. @@ -412,7 +413,7 @@ contain test images and videos that can be leveraged to evaluate our quantized m .. code-block:: Bash - [Docker] $ ./test_jpeg_classification resnet18_pt /workspace/examples/vai_library/samples/classification/images/002.jpg + [Docker] $ ./test_jpeg_classification resnet18_pt /workspace/examples/vai_library/samples/classification/images/001.jpg If you wish to do so, you can review the `result.jpg` file. OpenCV function calls have been used to overlay the predictions. diff --git a/docs/_sources/docs/quickstart/vek280.rst.txt b/docs/_sources/docs/quickstart/vek280.rst.txt index 6c6023d3c..38f0ef5ce 100644 --- a/docs/_sources/docs/quickstart/vek280.rst.txt +++ b/docs/_sources/docs/quickstart/vek280.rst.txt @@ -2,7 +2,12 @@ Quick Start Guide for Versal |trade| AI Edge VEK280 ################################################### -The AMD **DPUCV2DX** for Versal |trade| AI Edge is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support VEK280. +The AMD **DPUCV2DX8G** for Versal |trade| AI Edge is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support VEK280. + +.. image:: ../reference/images/VEK280_Top_img.png + :width: 400 + :align: center + ************* @@ -28,7 +33,7 @@ WSL This is an optional step intended to enable Windows users to evaluate Vitis |trade| AI. -Although this is not a fully tested and supported flow, in most cases users will be able to execute this basic tutorial on Windows. The Windows Subsystem for Linux (WSL) can be installed from the command line. Open a Powershell prompt as an Administrator and execute the following command: +Although this is not a fully supported flow, in most cases users will be able to execute this basic tutorial on Windows. The Windows Subsystem for Linux (WSL) can be installed from the command line. Open a Powershell prompt as an Administrator and execute the following command: .. code-block:: Bash @@ -138,6 +143,7 @@ Cross compile the ``resnet50_pt`` example. .. code-block:: Bash [Docker] $ cd examples/vai_runtime/resnet50_pt + [Docker] $ sudo chmod u+r+x build.sh [Docker] $ bash –x build.sh If the compilation process does not report an error and the executable file ``resnet50_pt`` is generated, then the host environment is installed correctly. If an error is reported, double-check that you executed the ``source ~/petalinux....`` command. @@ -216,7 +222,7 @@ If you are using a point-to-point connection or DHCP is not available, you can m Vitis-AI Model Zoo ================== -You can now select a model from the Vitis AI Model Zoo `Vitis AI Model Zoo <../workflow-model-zoo.html>`__. Navigate to the `model-list subdirectory `__ and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target. Choose the correct link for your target platform and download the model. +You can now select a model from the `Vitis AI Model Zoo <../workflow-model-zoo.html>`__. Navigate to the `model-list subdirectory `__ and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target. Choose the correct link for your target platform and download the model. 1. Take the ResNet50 model as an example. @@ -453,7 +459,7 @@ The Vitis AI Compiler compiles the graph operators as a set of micro-coded instr .. code-block:: Bash [Docker] $ cd /workspace/resnet18 - [Docker] $ vai_c_xir -x quantize_result/ResNet_int.xmodel -a /opt/vitis_ai/compiler/arch/DPUCV2DX/VEK280/arch.json -o resnet18_pt -n resnet18_pt + [Docker] $ vai_c_xir -x quantize_result/ResNet_int.xmodel -a /opt/vitis_ai/compiler/arch/DPUCV2DX8G/VEK280/arch.json -o resnet18_pt -n resnet18_pt - If compilation is successful, the ``resnet18_pt.xmodel`` file should be generated according to the specified DPU architecture. @@ -527,6 +533,7 @@ contain test images and videos that can be leveraged to evaluate our quantized m .. code-block:: Bash [Target] $ cd ~/Vitis-AI/examples/vai_library/samples/classification + [Target] $ chmod u+r+x build.sh [Target] $ ./build.sh 4. Execute the single-image test application. @@ -537,7 +544,7 @@ contain test images and videos that can be leveraged to evaluate our quantized m If you wish to do so, you can copy the `result.jpg` file back to your host and review the output. OpenCV function calls have been used to overlay the predictions. -5. To run the video example, run the following command. To keep this simple we will use one of the Vitis AI video samples, but you should scp your own video clip to the target (webm / raw formats). +5. To run the video example, run the following command. To keep this simple we will use one of the Vitis AI video samples, but users should scp their own video clip to the target in a webm or raw format. .. code-block:: Bash diff --git a/docs/_sources/docs/reference/release_notes.rst.txt b/docs/_sources/docs/reference/release_notes.rst.txt index 448659fb8..e1f341b4c 100644 --- a/docs/_sources/docs/reference/release_notes.rst.txt +++ b/docs/_sources/docs/reference/release_notes.rst.txt @@ -81,11 +81,14 @@ TensorFlow 1 CNN Quantizer - Support for setting the opset version in exporting onnx format. Bug Fixed: -1. Fixed a bug where the AddV2 operation is misunderstood as a BiasAdd. +1. Fixed a bug where the AddV2 operation is misinterpreted as a BiasAdd. Compiler -------- -- Release notes to be announced ASAP +- New operators supported: Broadcast add/mul, Bilinear downsample, Trilinear downsample, Group conv2d, Strided-slice +- Performance improved on XV2DPU +- Error message improved +- Compilation time speed up PyTorch Optimizer ----------------- @@ -124,7 +127,7 @@ Library Model Inspector --------------- -- Release notes to be announced ASAP +- Added support for DPUCV2DX8G Profiler -------- diff --git a/docs/_sources/docs/reference/version_compatibility.rst.txt b/docs/_sources/docs/reference/version_compatibility.rst.txt index 306498c62..4ce95c2e7 100644 --- a/docs/_sources/docs/reference/version_compatibility.rst.txt +++ b/docs/_sources/docs/reference/version_compatibility.rst.txt @@ -84,7 +84,7 @@ Zynq |trade| Ultrascale+ |trade| - 5.15 * - v2.0 - - 3.5 + - 3.4 - Vivado / Vitis / PetaLinux 2021.2 - 5.10 diff --git a/docs/_sources/docs/workflow-model-development.rst.txt b/docs/_sources/docs/workflow-model-development.rst.txt index f575cf4b0..7a0d2c472 100644 --- a/docs/_sources/docs/workflow-model-development.rst.txt +++ b/docs/_sources/docs/workflow-model-development.rst.txt @@ -17,7 +17,7 @@ In the early phases of development, it is highly recommended that the developer For more information on the Model Inspector, see the following resources: -- When you are ready to get started with the Vitis AI Model Inspector, refer to the examples provided for both `PyTorch `__ and `TensorFlow `__. +- When you are ready to get started with the Vitis AI Model Inspector, refer to the examples provided for both `PyTorch `__ and `TensorFlow `__. - If your graph uses operators that are not natively supported by your specific DPU target, see the :ref:`Operator Support ` section. @@ -129,13 +129,10 @@ Quantization Related Resources - For additional details on the Vitis AI Quantizer, refer the "Quantizing the Model" chapter in the `Vitis AI User Guide `__. -- TensorFlow 2.x examples are available as follows: - - `TF2 Post-Training Quantization `__ - - `TF2 Quantization Aware Training `__ +- TensorFlow 2.x examples are available `here `__ + +- PyTorch examples are available `here `__ -- PyTorch examples are available as follows: - - `PT Post-Training Quantization `__ - - `PT Quantization Aware Training `__ .. _model-compilation: diff --git a/docs/_sources/docs/workflow-model-zoo.rst.txt b/docs/_sources/docs/workflow-model-zoo.rst.txt index 9994aace7..3cec5f8bb 100644 --- a/docs/_sources/docs/workflow-model-zoo.rst.txt +++ b/docs/_sources/docs/workflow-model-zoo.rst.txt @@ -18,11 +18,16 @@ All the models in the Model Zoo are deployed on AMD adaptable hardware with `Vit To make the job of using the Model Zoo a little easier, we have provided a downloadable spreadsheet and an online table that incorporates key data about the Model Zoo models. The spreadsheet and tables include comprehensive information about all models, including links to the original papers and datasets, source framework, input size, computational cost (GOPs), and float and quantized accuracy. **You can download the spreadsheet** :download:`here `. +.. The below is functional (remove the .. comment on the second line) but has formatting issues that are currently unresolved. +.. raw:: html +.. :file: reference/ModelZoo_Github.htm + +.. For now we will just do this: .. raw:: html

Click here to view the Model Zoo Details & Performance table online.



-.. note:: Please note that if the models are marked as "Non-Commercial Use Only", users must comply with this `AMD license agreement `__ +.. note:: Please note that if the models are marked as "Non-Commercial Use Only", users must comply with this `AMD license agreement `__ .. note:: The model performance benchmarks listed in these tables are verified using Vitis AI v3.5 and Vitis AI Library v3.5. For each platform, specific DPU configurations are used and highlighted in the table's header. Free download of Vitis AI and Vitis AI Library from `Vitis AI Github `__ and `Vitis AI Library Github `__. diff --git a/docs/_sources/docs/workflow-system-integration.rst.txt b/docs/_sources/docs/workflow-system-integration.rst.txt index 2d13e9cab..9decad890 100644 --- a/docs/_sources/docs/workflow-system-integration.rst.txt +++ b/docs/_sources/docs/workflow-system-integration.rst.txt @@ -130,8 +130,8 @@ IP and Reference Designs * - DPUCZDX8G `PG338 `__ - MPSoC & Kria K26 - 3.0 - - `Download `__ - - `Get IP `__ + - `Download `__ + - `Get IP `__ * - DPUCVDX8G `PG389 `__ - VCK190 @@ -174,7 +174,7 @@ Vitis Integration The Vitis |trade| workflow specifically targets developers with a software-centric approach to AMD SoC system development. Vitis AI is differentiated from traditional FPGA flows, enabling you to build FPGA acceleration into your applications without developing RTL kernels. -The Vitis workflow enables the integration of the DPU IP as an acceleration kernel that is loaded at runtime in the form of an ``xclbin`` file. To provide developers with a reference platform that can be used as a starting point, the Vitis AI repository includes several `reference designs `__ for the different DPU architectures and target platforms. +The Vitis workflow enables the integration of the DPU IP as an acceleration kernel that is loaded at runtime in the form of an ``xclbin`` file. To provide developers with a reference platform that can be used as a starting point. For the DPUCV2DX8G, please refer to the VEK280 reference design included in this release. For MPSoC and Versal AI Core (non AIE-ML devices) please refer to the /dpu subdirectory in the Vitis AI 3.0 Github repository. In addition, a Vitis tutorial is available which provides the `end-to-end workflow `__ for creating a Vitis Platform for ZCU104 targets. @@ -213,7 +213,7 @@ There are two ways to integrate the Vitis |trade| AI Library and Runtime in a cu - Build the Linux image using Petalinux, incorporating the necessary recipes. -- Install Vitis AI 3.5 to the target leveraging a pre-built package at run time. For details of this procedure, please see :ref:`Vitis AI Online Installation ` +- Install Vitis AI 3.5 to the target leveraging a pre-built package at run time. For details of this procedure, please see the instructions in the Vitis AI Online Installation section below. .. _vart_vail_online_install: @@ -345,7 +345,7 @@ Run the following commands to upgrade PetaLinux. Following this upgrade, you will find ``vitis-ai-library_3.5.bb`` recipe in ``/components/yocto/layers/meta-vitis-ai``. -For details about this process, refer to `Petalinux Upgrade `__. +For details about this process, refer to `Petalinux Upgrade `__. .. note:: ``2023.1_update1`` will be released approximately 1 month after Vitis 3.5 release. The name of ``2023.1_update1`` may change. Modify it accordingly. diff --git a/docs/docs/install/Alveo_X11.html b/docs/docs/install/Alveo_X11.html index fbf0ff278..25e181a29 100644 --- a/docs/docs/install/Alveo_X11.html +++ b/docs/docs/install/Alveo_X11.html @@ -160,7 +160,7 @@

X11 Support for Running Vitis AI Docker with Alveo

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/China_Ubuntu_servers.html b/docs/docs/install/China_Ubuntu_servers.html index f5e4846c3..bc7630ccb 100644 --- a/docs/docs/install/China_Ubuntu_servers.html +++ b/docs/docs/install/China_Ubuntu_servers.html @@ -138,7 +138,7 @@

Access to Ubuntu Mirrors from within China
deb http://us.archive.ubuntu.com/ubuntu/ focal universe
-

You can see that the hostname “archive.ubuntu.com” resolves to servers located within the United States. When building the Vitis AI Docker image, whether for CPU-only or GPU applications Docker will attempt to pull from US servers. As a result, users accessing from China will generally experience slow download speeds.

+

You can see that the hostname “archive.ubuntu.com” resolves to servers located within the United States. When building the Vitis AI Docker image, whether for CPU-only or GPU accelerated containers Docker will attempt to pull from US servers. As a result, users accessing from China will generally experience slow download speeds.

Prior to building the Vitis AI Docker image it is recommended that you modify /etc/apt/sources.list and the vitis-ai-gpu.Dockerfile.

In the Vitis AI .DockerFile, change the first instances of apt-get update and apt-get install.

From:

@@ -184,7 +184,7 @@

Access to Ubuntu Mirrors from within China

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/Vitis AI 1.3.2 April 2021 Patch.html b/docs/docs/install/Vitis AI 1.3.2 April 2021 Patch.html index 587d14468..b0b0d75cf 100644 --- a/docs/docs/install/Vitis AI 1.3.2 April 2021 Patch.html +++ b/docs/docs/install/Vitis AI 1.3.2 April 2021 Patch.html @@ -181,7 +181,7 @@

Installation

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/Vitis AI 2.0 Feb 2022 Patch.html b/docs/docs/install/Vitis AI 2.0 Feb 2022 Patch.html index b9fdd955a..f10b043d7 100644 --- a/docs/docs/install/Vitis AI 2.0 Feb 2022 Patch.html +++ b/docs/docs/install/Vitis AI 2.0 Feb 2022 Patch.html @@ -191,7 +191,7 @@

Installation

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/Vitis AI 2.5 Aug 2022 Patch.html b/docs/docs/install/Vitis AI 2.5 Aug 2022 Patch.html index 74961ce90..23c0e48c8 100644 --- a/docs/docs/install/Vitis AI 2.5 Aug 2022 Patch.html +++ b/docs/docs/install/Vitis AI 2.5 Aug 2022 Patch.html @@ -181,7 +181,7 @@

Installation

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/branching_tagging_strategy.html b/docs/docs/install/branching_tagging_strategy.html index d187b445f..a6373ebc9 100644 --- a/docs/docs/install/branching_tagging_strategy.html +++ b/docs/docs/install/branching_tagging_strategy.html @@ -157,7 +157,7 @@

Branching / Tagging Strategy

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/install.html b/docs/docs/install/install.html index 9eabb828b..2c88991e7 100644 --- a/docs/docs/install/install.html +++ b/docs/docs/install/install.html @@ -498,7 +498,7 @@

Option 1: Leverage the Pre-Built Docker

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/install_docker.html b/docs/docs/install/install_docker.html index 1243c8c2b..2a7c83b6c 100644 --- a/docs/docs/install/install_docker.html +++ b/docs/docs/install/install_docker.html @@ -197,7 +197,7 @@

Docker Install

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/install/patch_instructions.html b/docs/docs/install/patch_instructions.html index 280e93421..5b73a0a4f 100644 --- a/docs/docs/install/patch_instructions.html +++ b/docs/docs/install/patch_instructions.html @@ -163,7 +163,7 @@

Installing a Vitis AI Patch

© Copyright 2022-2023, Advanced Micro Devices, Inc. - Last updated on June 29, 2023. + Last updated on July 2, 2023.

diff --git a/docs/docs/quickstart/v70.html b/docs/docs/quickstart/v70.html index 06718ea2f..a3f3157f5 100644 --- a/docs/docs/quickstart/v70.html +++ b/docs/docs/quickstart/v70.html @@ -160,7 +160,7 @@

Quick Start Guide for Alveo V70

-

The AMD DPUCV2DX8G for the Alveo™ V70 is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support V70.

+

The AMD DPUCV2DX8G for the Alveo™ V70 is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you install the software and packages required to support V70.

../../_images/V70.PNG

Prerequisites

@@ -252,7 +252,7 @@

Docker Container Environment Variable Setup

Vitis-AI Model Zoo

-

You can now select a model from the Vitis AI Model Zoo Vitis AI Model Zoo. Navigate to the model-list subdirectory and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target. Choose the correct link for your target platform and download the model.

+

You can now select a model from the Vitis AI Model Zoo. Navigate to the model-list subdirectory and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target. Choose the correct link for your target platform and download the model.

  • Take the ResNet50 model as an example.

@@ -279,7 +279,7 @@

Run the Vitis AI Examples

  • Extract the vitis_ai_runtime_r3.5.0_image_video.tar.gz package.

    • -
    [Docker] $ tar -xzvf vitis_ai_runtime_r3.5.0_image_video.tar.gz -C /w/examples/vai_runtime
+
    [Docker] $ tar -xzvf vitis_ai_runtime_r3.5.0_image_video.tar.gz -C /workspace/examples/vai_runtime
 
    
 
    
 
      
@@ -291,7 +291,8 @@ 
      Run the Vitis AI Examples
 
    1. Compile the example.
      2. 
 
    
-
    [Docker] $ bash -x build.sh
+
    [Docker] $ sudo chmod u+r+x build.sh
+[Docker] $ bash -x build.sh
 
    
 
    
 
      
@@ -538,7 +539,7 @@ 
      Model Deployment
 
    1. Execute the single-image test application.
      2. 
 
    
-
    [Docker] $ ./test_jpeg_classification resnet18_pt /workspace/examples/vai_library/samples/classification/images/002.jpg
+
    [Docker] $ ./test_jpeg_classification resnet18_pt /workspace/examples/vai_library/samples/classification/images/001.jpg
 
    
 
    
 
    If you wish to do so, you can review the result.jpg file.  OpenCV function calls have been used to overlay the predictions.
    
@@ -573,7 +574,7 @@ 
    Model Deployment
     
    © Copyright 2022-2023, Advanced Micro Devices, Inc.
-      Last updated on June 29, 2023.
+      Last updated on July 2, 2023.
       
    
   
    
 
diff --git a/docs/docs/quickstart/vek280.html b/docs/docs/quickstart/vek280.html
index 5626decf9..aaed4760f 100644
--- a/docs/docs/quickstart/vek280.html
+++ b/docs/docs/quickstart/vek280.html
@@ -161,7 +161,8 @@
              
   
    
 
    Quick Start Guide for Versal™ AI Edge VEK280
    
-
    The AMD DPUCV2DX for Versal™ AI Edge is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support VEK280.
    
+
    The AMD DPUCV2DX8G for Versal™ AI Edge is a configurable computation engine dedicated to convolutional neural networks. It supports a highly optimized instruction set, enabling the deployment of most convolutional neural networks. The following instructions will help you to install the software and packages required to support VEK280.
    
+../../_images/VEK280_Top_img.png
 
    
 
    Prerequisites
    
 
    
@@ -180,7 +181,7 @@ 
    Applicable Targets
 
    WSL
    
 
    This is an optional step intended to enable Windows users to evaluate Vitis™ AI.
    
-
    Although this is not a fully tested and supported flow, in most cases users will be able to execute this basic tutorial on Windows.  The Windows Subsystem for Linux (WSL) can be installed from the command line. Open a Powershell prompt as an Administrator and execute the following command:
    
+
    Although this is not a fully supported flow, in most cases users will be able to execute this basic tutorial on Windows.  The Windows Subsystem for Linux (WSL) can be installed from the command line. Open a Powershell prompt as an Administrator and execute the following command:
    
 
    [Powershell] > wsl --install -d Ubuntu-20.04
 
    
 
    
@@ -268,6 +269,7 @@ 
    Setup the Hostresnet50_pt example.
    
 
    [Docker] $ cd examples/vai_runtime/resnet50_pt
+[Docker] $ sudo chmod u+r+x build.sh
 [Docker] $ bash –x build.sh
 
    
 
    
@@ -346,7 +348,7 @@ 
    Setup the Target
 
    Vitis-AI Model Zoo
    
-
    You can now select a model from the Vitis AI Model Zoo Vitis AI Model Zoo.  Navigate to the  model-list subdirectory  and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target.  Choose the correct link for your target platform and download the model.
    
+
    You can now select a model from the Vitis AI Model Zoo.  Navigate to the  model-list subdirectory  and select the model that you wish to test. For each model, a YAML file provides key details of the model. In the YAML file there are separate hyperlinks to download the model for each supported target.  Choose the correct link for your target platform and download the model.
    
 
      
 
    1. Take the ResNet50 model as an example.
      2. 
 
    
@@ -565,7 +567,7 @@ 
    Compile the ModelINT8.xmodel and generates the deployable DPU.xmodel by running the command below.  Note that you must modify the command to specify the appropriate arch.json file for your target.  For MPSoC targets, these are located in the folder /opt/vitis_ai/compiler/arch/DPUCZDX8G inside the Docker container.
    
 
 
    [Docker] $ cd /workspace/resnet18
-[Docker] $ vai_c_xir -x quantize_result/ResNet_int.xmodel -a /opt/vitis_ai/compiler/arch/DPUCV2DX/VEK280/arch.json -o resnet18_pt -n resnet18_pt
+[Docker] $ vai_c_xir -x quantize_result/ResNet_int.xmodel -a /opt/vitis_ai/compiler/arch/DPUCV2DX8G/VEK280/arch.json -o resnet18_pt -n resnet18_pt
 
    
 
    
 
    
@@ -651,7 +654,7 @@ 
    Model Deployment
-
  • To run the video example, run the following command.  To keep this simple we will use one of the Vitis AI video samples, but you should scp your own video clip to the target (webm / raw formats).
    • 
+
  • To run the video example, run the following command.  To keep this simple we will use one of the Vitis AI video samples, but users should scp their own video clip to the target in a webm or raw format.
    • 
 
 
    [Target] $ ./test_video_classification resnet18_pt ~/Vitis-AI/examples/vai_library/apps/pose_960_540.avi -t 8
 
    
@@ -694,7 +697,7 @@ 
    Model Deployment
     
    © Copyright 2022-2023, Advanced Micro Devices, Inc.
-      Last updated on June 29, 2023.
+      Last updated on July 2, 2023.
       
    
   
    
 
diff --git a/docs/docs/ref_design_docs/README_DPUCV2DX8G.html b/docs/docs/ref_design_docs/README_DPUCV2DX8G.html
index b4c6a0f33..60cf2197e 100644
--- a/docs/docs/ref_design_docs/README_DPUCV2DX8G.html
+++ b/docs/docs/ref_design_docs/README_DPUCV2DX8G.html
@@ -599,7 +599,7 @@ 
    8 Known Issues
     
    © Copyright 2022-2023, Advanced Micro Devices, Inc.
-      Last updated on June 29, 2023.
+      Last updated on July 2, 2023.
       
    
   
    
 
diff --git a/docs/docs/reference/ModelZoo_Github_web.htm b/docs/docs/reference/ModelZoo_Github_web.htm
index 2d7716d9a..38c3cedc8 100644
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+++ b/docs/docs/reference/ModelZoo_Github_web.htm
@@ -1,6507 +1,449 @@
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    Vitis AI 3.5 Model Zoo
    Copyright (c) 2023 Advanced Micro Devices, Inc.
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