boost_cv_by_flycv.md

简体中文 | English

Accelerate end-to-end inference performance using FlyCV

FlyCV is a high performance computer image processing library, providing better performance than other image processing libraries, especially in the ARM architecture. FastDeploy is now integrated with FlyCV, allowing users to use FlyCV on supported hardware platforms to accelerate model end-to-end inference performance.

Supported OS and Architectures

OS	Architectures
Android	armeabi-v7a, arm64-v8a
Linux	aarch64, armhf, x86_64

Usage

To use FlyCV, you first need to turn on the FlyCV compile option at compile time, and then add a new line of code to turn it on. This article uses Linux as an example to show how to enable the FlyCV compile option, and then add a new line of code to use FlyCV during deployment.

You can turn on the FlyCV compile option when compiling the FastDeploy library as follows.

# When compiling C++ libraries
-DENABLE_VISION=ON

#  When compiling Python libraries
export ENABLE_FLYCV=ON

You can enable FlyCV by adding a new line of code to the deployment code as follows.

# C++ code
fastdeploy::vision::EnableFlyCV();
# Other..(e.g. With Huawei Ascend)
fastdeploy::RuntimeOption option;
option.UseAscend();
...


# Python code
fastdeploy.vision.enable_flycv()
# Other..(e.g. With Huawei Ascend)
runtime_option = build_option()
option.use_ascend()
...

Some Platforms FlyCV End-to-End Inference Performance

KunPeng 920 CPU + Atlas 300I Pro.

Model	OpenCV E2E Performance(ms)	FlyCV E2E Performance(ms)
ResNet50	2.78	1.63
PP-LCNetV2	2.50	1.39
YOLOv7	27.00	21.36
PP_HumanSegV2_Lite	2.76	2.10

Rockchip RV1126.

Model	OpenCV E2E Performance(ms)	FlyCV E2E Performance(ms)
ResNet50	9.23	6.01
mobilenetv1_ssld_量化模型	9.23	6.01
yolov5s_量化模型	28.33	14.25
PP_LiteSeg_量化模型	132.25	60.31