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rknpu2.md 2.96 KB

English | [中文](../../../cn/faq/rknpu2/rknpu2.md) 
# RKNPU2 Model Deployment

## Installation Environment
RKNPU2 model export is only supported on x86 Linux platform, please refer to [RKNPU2 Model Export Environment Configuration](./install_rknn_toolkit2.md).

## Convert ONNX to RKNN
Since the ONNX model cannot directly calculate by calling the NPU, it is necessary to convert the ONNX model to RKNN model. For detailed information, please refer to [RKNPU2 Conversion Document](./export.md).

## Models supported for RKNPU2
The following tests are at end-to-end speed, and the test environment is as follows:
* Device Model: RK3588
* ARM CPU is tested on ONNX
* with single-core NPU

| Mission Scenario                 | Model                                                                                       | Model Version(tested version)          | ARM CPU/RKNN speed(ms) |
|----------------------|------------------------------------------------------------------------------------------|--------------------------|--------------------|
| Detection            | [Picodet](../../../../examples/vision/detection/paddledetection/rknpu2/README.md)        | Picodet-s                | 162/112            |
| Detection            | [RKYOLOV5](../../../../examples/vision/detection/rkyolo/README.md)                       | YOLOV5-S-Relu(int8)      | -/57               |
| Detection            | [RKYOLOX](../../../../examples/vision/detection/rkyolo/README.md)                        | -                        | -/-                |
| Detection            | [RKYOLOV7](../../../../examples/vision/detection/rkyolo/README.md)                       | -                        | -/-                |
| Segmentation         | [Unet](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md)              | Unet-cityscapes          | -/-                |
| Segmentation         | [PP-HumanSegV2Lite](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | portrait(int8)           | 133/43             |
| Segmentation         | [PP-HumanSegV2Lite](../../../../examples/vision/segmentation/paddleseg/rknpu2/README.md) | human(int8)              | 133/43             |
| Face Detection       | [SCRFD](../../../../examples/vision/facedet/scrfd/rknpu2/README.md)                      | SCRFD-2.5G-kps-640(int8) | 108/42             |
| Face FaceRecognition | [InsightFace](../../../../examples/vision/faceid/insightface/rknpu2/README_CN.md)        | ms1mv3_arcface_r18(int8) | 81/12              |
| Classification       | [ResNet](../../../../examples/vision/classification/paddleclas/rknpu2/README.md)         | ResNet50_vd              | -/33               |

## Download Pre-trained library

For convenience, here we provide the 1.0.2 version of FastDeploy.

- [FastDeploy RK356X c++ SDK](https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-aarch64-rk356X-1.0.2.tgz)
- [FastDeploy RK3588 c++ SDK](https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-aarch64-rk3588-1.0.2.tgz)

一键复制编辑原始数据按行查看历史

提交于 2023-01-28 19:01 . [Doc]Update README_EN.md (#1173)

English | 中文

RKNPU2 Model Deployment

Installation Environment

RKNPU2 model export is only supported on x86 Linux platform, please refer to RKNPU2 Model Export Environment Configuration.

Convert ONNX to RKNN

Since the ONNX model cannot directly calculate by calling the NPU, it is necessary to convert the ONNX model to RKNN model. For detailed information, please refer to RKNPU2 Conversion Document.

Models supported for RKNPU2

The following tests are at end-to-end speed, and the test environment is as follows:

Device Model: RK3588
ARM CPU is tested on ONNX
with single-core NPU

Mission Scenario	Model	Model Version(tested version)	ARM CPU/RKNN speed(ms)
Detection	Picodet	Picodet-s	162/112
Detection	RKYOLOV5	YOLOV5-S-Relu(int8)	-/57
Detection	RKYOLOX	-	-/-
Detection	RKYOLOV7	-	-/-
Segmentation	Unet	Unet-cityscapes	-/-
Segmentation	PP-HumanSegV2Lite	portrait(int8)	133/43
Segmentation	PP-HumanSegV2Lite	human(int8)	133/43
Face Detection	SCRFD	SCRFD-2.5G-kps-640(int8)	108/42
Face FaceRecognition	InsightFace	ms1mv3_arcface_r18(int8)	81/12
Classification	ResNet	ResNet50_vd	-/33

Download Pre-trained library

For convenience, here we provide the 1.0.2 version of FastDeploy.

⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit for ☁️Cloud 📱Mobile and 📹Edge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models.

https://www.paddlepaddle.org.cn/fastdeploy

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