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Merge pull request #1037 from EESN-W/dragon-q6a
feat: dragon q6a npu dev demos
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‎docs/common/radxa-os/build-system/_install_env.mdx‎

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主要介绍 U-Boot、Kernel、RadxaOS 开发的环境搭建。
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主要介绍 {props?.items ?? 'U-Boot、Kernel and Radxa OS'} 开发的环境搭建。
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## 使用前提
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## 环境搭建
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我们需要在 PC 上安装 Visual Studio Code、Dev Container 拓展插件和 Docker，方便快速构建 U-Boot、Kernel、RadxaOS 的编译和开发环境。
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我们需要在 PC 上安装 Visual Studio Code、Dev Container 拓展插件和 Docker，方便快速构建 {props?.items ?? 'U-Boot、Kernel and Radxa OS'} 的编译和开发环境。
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### Docker
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‎docs/dragon/q6a/faq.md‎

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</NewCodeBlock>
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3. 检查串口设备是否被其他程序占用
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## 为什么 Radxa OS 默认使用 GDM 而不是 SDDM？
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Radxa OS 默认使用 GDM（GNOME Display Manager）替代 SDDM（Simple Desktop Display Manager），主要原因如下：
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**SDDM 的局限性：**
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- 在 HDMI 未连接的 headless 模式下，SDDM 无法正常启动图形界面
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- 这会导致用户无法通过远程桌面或 VNC 等方式登录系统
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**GDM 的优势：**
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- 在各种连接状态下（包括 headless 模式）都能稳定运行
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- 提供更好的兼容性和可靠性
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如果您希望切换回 SDDM，请按以下步骤操作：
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<NewCodeBlock tip="Linux$" type="host">
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```bash
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sudo dpkg-reconfigure sddm
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```
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</NewCodeBlock>
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在弹出的配置界面中，选择 `sddm` 作为默认显示管理器，然后重启系统即可使更改生效。

‎docs/dragon/q6a/low-dev/build-system/install-env.md‎

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# 环境搭建
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<InstallEnv />
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<InstallEnv items="Kernel and Radxa OS" />

‎i18n/en/docusaurus-plugin-content-docs/current/common/ai/_aotgan_qai-app-builder.mdx‎

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:::tip
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1. Please install QAI AppBuilder according to the [**QAI AppBuilder Installation Guide**](../qai-appbuilder).
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1. Please install QAI AppBuilder according to [**QAI AppBuilder Installation Guide**](../qai-appbuilder#installation-methods).
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2. Configure QAIRT environment variables as described in [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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2. Please configure QAIRT environment variables according to [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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:::
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## Run the Example

‎i18n/en/docusaurus-plugin-content-docs/current/common/ai/_beit_qai-app-builder.mdx‎

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:::tip
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1. Please install QAI AppBuilder according to the [**QAI AppBuilder Installation Guide**](../qai-appbuilder).
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1. Please install QAI AppBuilder according to [**QAI AppBuilder Installation Guide**](../qai-appbuilder#installation-methods).
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2. Configure QAIRT environment variables as described in [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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2. Please configure QAIRT environment variables according to [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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:::
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## Run the Example

‎i18n/en/docusaurus-plugin-content-docs/current/common/ai/_convnext_base_qai-app-builder.mdx‎

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This document describes using the [QAI AppBuilder](../qai-appbuilder) Python API to perform inference with the [ConvNext-Base](https://aihub.qualcomm.com/models/convnext_base)
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target recognition model using Qualcomm® Hexagon™ Processor (NPU).
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**Supported Devices**
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| Device | SoC |
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| ---------- | ------- |
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| Dragon Q6A | QCS6490 |
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## Install QAI AppBuilder
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:::tip
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1. Please install QAI AppBuilder according to [**QAI AppBuilder Installation Guide**](../qai-appbuilder#installation-methods).
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2. Please configure QAIRT environment variables according to [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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:::
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## Run Example
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### Install Dependencies
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<NewCodeBlock tip="Device" type="device">
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```bash
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pip3 install requests tqdm qai-hub py3_wget opencv-python torch torchvision
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```
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</NewCodeBlock>
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### Run Script
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- Navigate to the example directory
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<Tabs>
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<TabItem value="QCS6490">
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<NewCodeBlock tip="Device" type="device">
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```bash
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cd ai-engine-direct-helper/samples/linux/python
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```
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</NewCodeBlock>
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</TabItem>
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</Tabs>
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- Prepare input image, using the following image as input example
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{" "}
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<div style={{ textAlign: "center" }}>
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<img src="/en/img/dragon/q6a/test_image.webp" style={{ width: "65%" }} />
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input image
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</div>
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- Execute inference
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<NewCodeBlock tip="Device" type="device">
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```bash
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python3 convnext_base/convnext_base.py
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```
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</NewCodeBlock>
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```bash
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$ python3 convnext_base/convnext_base.py
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Current file directory: /mnt/ssd/qualcomm/701/zzf_fork/ai-engine-direct-helper/samples/linux/python/convnext_base
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0.0ms [WARNING] <W> Initializing HtpProvider
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/prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:38:dummy call to rpcmem_init, rpcmem APIs will be used from libxdsprpc
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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193.9ms [WARNING] Time: Read model file to memory. 71.69
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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269.5ms [WARNING] Time: contextCreateFromBinary. 75.47
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269.5ms [WARNING] Time: UnmapViewOfFile. 0.00
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270.9ms [WARNING] Time: model_initialize convnext_base 270.85
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348.3ms [WARNING] Time: model_inference convnext_base 30.17
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Top 5 predictions for image:
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Samoyed 0.8726790547
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Pomeranian 0.0192712061
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keeshond 0.0150661934
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Japanese spaniel 0.0032834315
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Eskimo dog 0.0020474677
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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/prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:42:dummy call to rpcmem_deinit, rpcmem APIs will be used from libxdsprpc
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365.5ms [WARNING] Time: model_destroy convnext_base 14.44
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```
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The printed results show that `Samoyed` has the highest confidence, which matches the input image content.

‎i18n/en/docusaurus-plugin-content-docs/current/common/ai/_convnext_tiny_qai-app-builder.mdx‎

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This document describes using the [QAI AppBuilder](../qai-appbuilder) Python API to perform inference with the [ConvNext-Tiny](https://aihub.qualcomm.com/models/convnext_tiny)
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target recognition model using Qualcomm® Hexagon™ Processor (NPU).
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**Supported Devices**
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| Device | SoC |
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| ---------- | ------- |
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| Dragon Q6A | QCS6490 |
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## Install QAI AppBuilder
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:::tip
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1. Please install QAI AppBuilder according to [**QAI AppBuilder Installation Guide**](../qai-appbuilder).
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2. Please configure QAIRT environment variables according to [**Configure QAIRT Environment Variables**](../qai-appbuilder).
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:::
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## Run Example
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### Install Dependencies
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<NewCodeBlock tip="Device" type="device">
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```bash
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pip3 install requests tqdm qai-hub py3_wget opencv-python torch torchvision
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```
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</NewCodeBlock>
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### Run Script
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- Navigate to the example directory
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<Tabs>
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<TabItem value="QCS6490">
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<NewCodeBlock tip="Device" type="device">
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```bash
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cd ai-engine-direct-helper/samples/linux/python
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```
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</NewCodeBlock>
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</TabItem>
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</Tabs>
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- Prepare input image, using the following image as input example
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{" "}
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<div style={{ textAlign: "center" }}>
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<img src="/en/img/dragon/q6a/test_image.webp" style={{ width: "65%" }} />
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input image
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</div>
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- Execute inference
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<NewCodeBlock tip="Device" type="device">
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```bash
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python3 convnext_tiny/convnext_tiny.py
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```
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</NewCodeBlock>
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```bash
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$ python3 convnext_tiny/convnext_tiny.py
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Current file directory: /mnt/ssd/qualcomm/701/zzf_fork/ai-engine-direct-helper/samples/linux/python/convnext_tiny
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0.0ms [WARNING] <W> Initializing HtpProvider
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/prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:38:dummy call to rpcmem_init, rpcmem APIs will be used from libxdsprpc
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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132.1ms [WARNING] Time: Read model file to memory. 15.88
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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176.1ms [WARNING] Time: contextCreateFromBinary. 44.01
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176.2ms [WARNING] Time: UnmapViewOfFile. 0.00
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176.6ms [WARNING] Time: model_initialize convnext_tiny 176.53
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245.2ms [WARNING] Time: model_inference convnext_tiny 11.95
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Top 5 predictions for image:
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Samoyed 0.637629509
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Pomeranian 0.1382825226
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keeshond 0.0217001885
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Arctic fox 0.0138142202
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white wolf 0.0115938596
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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0.0ms [WARNING] <W> This META does not have Alloc2 Support
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/prj/qct/webtech_scratch20/mlg_user_admin/qaisw_source_repo/rel/qairt-2.37.1/point_release/SNPE_SRC/avante-tools/prebuilt/dsp/hexagon-sdk-5.4.0/ipc/fastrpc/rpcmem/src/rpcmem_android.c:42:dummy call to rpcmem_deinit, rpcmem APIs will be used from libxdsprpc
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263.1ms [WARNING] Time: model_destroy convnext_tiny 15.29
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```
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The printed results show that `Samoyed` has the highest confidence, which matches the input image content.
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---

‎i18n/en/docusaurus-plugin-content-docs/current/common/ai/_depth_anything_qai-app-builder.mdx‎

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:::tip
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1. Please install QAI AppBuilder according to the [**QAI AppBuilder Installation Guide**](../qai-appbuilder).
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1. Please install QAI AppBuilder according to [**QAI AppBuilder Installation Guide**](../qai-appbuilder#installation-methods).
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2. Configure QAIRT environment variables as described in [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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2. Please configure QAIRT environment variables according to [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
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## Run the Example

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