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The printed results show that `Samoyed` has the highest confidence, which matches the input image content.
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---
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## English Translation (For Review)
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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#installation-methods).
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2. Please configure QAIRT environment variables according to [**Configure QAIRT Environment Variables**](../qai-appbuilder#configure-qairt-environment-variables).
Current file directory: /mnt/ssd/qualcomm/701/zzf_fork/ai-engine-direct-helper/samples/linux/python/convnext_tiny
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# FCN-ResNet50 Example
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import FCNRESNRT50QAIAPPBUILDER from '../../../../../common/ai/\_fcn-resnet50_qai-app-builder.mdx';
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import FCNRESNET50QAIAPPBUILDER from '../../../../../common/ai/\_fcn-resnet50_qai-app-builder.mdx';
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