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rknn_model_zoo's Introduction

RKNN模型库

​ Rockchip Neural Network(RKNN)是瑞芯微为了加速模型推理而基于自身NPU硬件架构定义的一套模型格式.使用该格式定义的模型在Rockchip NPU上可以获得远高于CPU/GPU的性能.
​ RKNN Model Zoo是由像您这样的社区成员贡献的覆盖不同任务,不同框架的SOTA模型的集合.它涵盖了模型转换,模型评估和模型部署等基于Rockchip NPU的完整AI应用开发流程. 每个模型都包含模型转换,模型评估,模型部署相关的脚本,如果有对模型原始工程调整的,还会提供相应的脚本或补丁.


相关模型权重请从百度网盘获取，密码为 rknn

(网盘提供的 RKNN 模型如无特别说明，则基于最新 release 版本的 RKNN-Toolkit1/2 生成，使用 RKNN 模型时请先将设备的 NPU 驱动更新至最新的 release 版本；如无法更新驱动，则需要使用旧版本 RKNN-Toolkit 生成与驱动匹配的 RKNN 模型)

更新简述

    2022-11-15 模型新增:
     1.新增 yolov7,yolox 支持
     2.新增 lite-transformer 支持。(beta版本)
功能优化:
     1.RKNN-convert新增 capi test 功能。(beta版本)
     2.新增定频工具

模型概览

目标检测

这组模型的作用是识别图像中出现的物体,给出物体所在位置和物体的类别.

模型 说明
YOLO 支持 yolo 系列的检测模型，目前包含 yolov5/ yolov7/ yolox，支持 RKNN-Toolkit1/2

语言翻译

实现不同语言的翻译，如中英翻译

模型 说明
Lite transformer 以英文转中文翻译作为示例，支持 RKNN-Toolkit1/2

Acknowledgements

    expand https://github.com/ultralytics/yolov5
https://github.com/WongKinYiu/yolov7
https://github.com/Megvii-BaseDetection/YOLOX
https://github.com/mit-han-lab/lite-transformer

rknn_model_zoo's People

Contributors

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