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Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com>
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English | 简体中文

InsightFace RKNPU Deployment Example

This document provides the deployment of the InsightFace model in the RKNPU2 environment. For details, please refer to [Model Introduction Document].本教程提供InsightFace模型在RKNPU2环境下的部署，模型的详细介绍已经ONNX模型的下载请查看模型介绍文档。

支持模型列表

目前FastDeploy支持如下模型的部署

  • ArcFace
  • CosFace
  • PartialFC
  • VPL

下载预训练ONNX模型

为了方便开发者的测试，下面提供了InsightFace导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）其中精度指标来源于InsightFace中对各模型的介绍，详情各参考InsightFace中的说明

模型 大小 精度 (AgeDB_30)
CosFace-r18 92MB 97.7
CosFace-r34 131MB 98.3
CosFace-r50 167MB 98.3
CosFace-r100 249MB 98.4
ArcFace-r18 92MB 97.7
ArcFace-r34 131MB 98.1
ArcFace-r50 167MB -
ArcFace-r100 249MB 98.4
ArcFace-r100_lr0.1 249MB 98.4
PartialFC-r34 167MB -
PartialFC-r50 249MB -

转换为RKNPU模型

wget https://bj.bcebos.com/paddlehub/fastdeploy/ms1mv3_arcface_r18.onnx

python -m paddle2onnx.optimize --input_model ./ms1mv3_arcface_r18/ms1mv3_arcface_r18.onnx \
                               --output_model ./ms1mv3_arcface_r18/ms1mv3_arcface_r18.onnx \
                               --input_shape_dict "{'data':[1,3,112,112]}"

python  /Path/To/FastDeploy/tools/rknpu2/export.py \
        --config_path tools/rknpu2/config/arcface_unquantized.yaml \
        --target_platform rk3588

详细部署文档

版本说明