OAID/TengineKit

OAID/TengineKit has 2,319 GitHub stars and 308 forks. GitHot measured 0 stars in 24 hours and +1 in 7 days. Last checked 2026-11-07 12:01 UTC. Last push 2021-10-18.

How these counts are measured

TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.

2,319 stars 308 forks

What you can do

face 3dlandmarks
face attributes for example: age, gender, smile, glasses
eye iris & landmarks
hand detect(Real-time, not yet on Mobile)

Measured star growth

in 24h: 0
in 7 days: +1
in 15 days: Collecting history
in 1 month: Collecting history

Net star changes over overlapping periods, not daily totals. Missing measurements are left blank. Stars measure attention, not software quality.

README.md

Original

中文版本

TengineKit, developed by OPEN AI LAB.
TengineKit is an easy-to-integrate AI algorithm SDK. At present, it can run on various mobile phones at very low latency.We will continue to update this project for better results and better performance!

Effect

Face Detection & Face 2dLandmark	Face 3dLandmark & Iris	Upper Body Detection & Uppper Body Landmark	Hand Detection & Hand Landmark

Gif

dance of host

Video( YouTube | BiliBili )

Have a try

Apk can be directly downloaded and installed on the phone to see the effect.

scan code to download apk

Apk

Goals

Provide best performance in mobile client
Provide the simplest API in mobile client
Provide the smallest package in mobile client

Features

face detection
face landmarks
face 3dlandmarks
face attributes for example: age, gender, smile, glasses
eye iris & landmarks
body detect
hand detect(Real-time, not yet on Mobile)
hand landmarks(Real-time, not yet on Mobile)
body detect google(Real-time, not yet on Mobile)
body landamrks(Real-time, not yet on Mobile)
yolov5

Update (2021/03/25)

Fixed Linux sample code errer
Update Android sample code, up fps
update Linux so file
update Linux yolov5s
Fixed memory(Core v0.0.6)

Performance(Face Detect & Face Landmark)

CPU	Time consuming	Frame rate
Kirin 980	4ms	250fps
Qualcomm 855	5ms	200fps
Kirin 970	7ms	142fps
Qualcomm 835	8ms	125fps
Kirin 710F	9ms	111fps
Qualcomm 439	16ms	62fps
MediaTek Helio P60	17ms	59fps
Qualcomm 450B	18ms	56fps

Landmark Points Order

Contact

About the use of TengineKit and face-related technical exchanges, you can join the following QQ groups(Group Answer:TengineKit):

TengineKit communication QQ group: 630836519
Scan to join group