InspireFace

InspireFace is a cross-platform face recognition SDK developed in C/C++, supporting multiple operating systems and various backend types for inference, such as CPU, GPU, and NPU.

From HyperInspire·Updated June 22, 2026·View on GitHub·

InspireFace is a cross-platform face recognition SDK developed in C/C++, supporting multiple operating systems and various backend types for inference, such as CPU, GPU, and NPU. The project is written primarily in C++, first published in 2023. Key topics include: face, face-alignment, face-detection, face-mask-detection, face-recognition.

Latest release: v1.2.3— InspireFace v1.2.3

August 8, 2025View Changelog →

InspireFace

InspireFace is a cross-platform face recognition SDK developed in C/C++, supporting multiple operating systems and various backend types for inference, such as CPU, GPU, and NPU.

If you require further information on tracking development branches, CI/CD processes, or downloading pre-compiled libraries, please visit our development repository.

Please contact contact@insightface.ai using your company e-mail for commercial support, including obtaining and integrating higher accuracy models, as well as custom development.

📘 Documentation is a work in progress.
We welcome your questions💬, they help guide and accelerate its development.

Change Logs

2025-08-03 Add a multi-link model download channel for the Python-SDK.

2025-06-15 The ErrorCode-Table has been reorganized and streamlined.

2025-06-08 Add facial expression recognition.

2025-04-27 Optimize some issues and provide a stable version.

2025-03-16 Acceleration using NVIDIA-GPU (CUDA) devices is already supported.

2025-03-09 Release of android sdk in JitPack.

2025-02-20 Upgrade the face landmark model.

2025-01-21 Update all models to t3 and add tool to convert cosine similarity to percentage.

2025-01-08 Support inference on Rockchip devices RK3566/RK3568 NPU.

2024-12-25 Add support for optional RKRGA image acceleration processing on Rockchip devices.

2024-12-22 Started adapting for multiple Rockchip devices with NPU support, beginning with RV1103/RV1106 support.

2024-12-10 Added support for quick installation via Python package manager.

2024-10-09 Added system resource monitoring and session statistics.

2024-09-30 Fixed some bugs in the feature hub.

2024-08-18 Updating Benchmark: Using CoreML with Apple's Neural Engine (ANE) on the iPhone 13, the combined processes of Face Detection + Alignment + Feature Extraction take less than 2ms.

2024-07-17 Add global resource statistics monitoring to prevent memory leaks.

2024-07-07 Add some face action detection to the face interaction module.

2024-07-05 Fixed some bugs in the python ctypes interface.

2024-07-03 Add the blink detection algorithm of face interaction module.

2024-07-02 Fixed several bugs in the face detector with multi-level input.

License

The licensing of the open-source models employed by InspireFace adheres to the same requirements as InsightFace, specifying their use solely for academic purposes and explicitly prohibiting commercial applications.

Quick Start

For Python users on Linux and MacOS, InspireFace can be quickly installed via pip:

bash
pip install -U inspireface

After installation, you can use inspireface like this:

Python
import cv2
import inspireface as isf

# Create a session with optional features
opt = isf.HF_ENABLE_NONE
session = isf.InspireFaceSession(opt, isf.HF_DETECT_MODE_ALWAYS_DETECT)

# Load the image using OpenCV.
image = cv2.imread(image_path)

# Perform face detection on the image.
faces = session.face_detection(image)

for face in faces:
    x1, y1, x2, y2 = face.location
    rect = ((x1, y1), (x2, y2), face.roll)
     # Calculate center, size, and angle
    center = ((x1 + x2) / 2, (y1 + y2) / 2)
    size = (x2 - x1, y2 - y1)
    angle = face.roll

    # Apply rotation to the bounding box corners
    rect = ((center[0], center[1]), (size[0], size[1]), angle)
    box = cv2.boxPoints(rect)
    box = box.astype(int)

    # Draw the rotated bounding box
    cv2.drawContours(image, [box], 0, (100, 180, 29), 2)

cv2.imshow("face detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

⚠️The project is currently in a rapid iteration phase, before each update, please pull the latest model from the remote side!

python
import inspireface

for model in ["Pikachu", "Megatron"]:
    inspireface.pull_latest_model(model)

More examples can be found in the python directory.

Preparation

Clone 3rdparty

Clone the 3rdparty repository from the remote repository into the root directory of the project. Note that this repository contains some submodules. When cloning, you should use the --recurse-submodules parameter, or after entering the directory, use git submodule update --init --recursive to fetch and synchronize the latest submodules:

Bash
# Must enter this directory
cd InspireFace
# Clone the repository and pull submodules
git clone --recurse-submodules https://github.com/tunmx/inspireface-3rdparty.git 3rdparty

If you need to update the 3rdparty repository to ensure it is current, or if you didn't use the --recursive parameter during the initial pull, you can run git submodule update --init --recursive:

bash
# Must enter this directory
cd InspireFace
# If you're not using recursive pull
git clone https://github.com/tunmx/inspireface-3rdparty.git 3rdparty

cd 3rdparty
git pull
# Update submodules
git submodule update --init --recursive

Downloading Model Package Files

You can download the model package files containing models and configurations needed for compilation from Release Page and extract them to any location.

You can use the command/download_models_general.sh command to download resource files, which will be downloaded to the test_res/pack directory. This way, when running the Test program, it can access and read the resource files from this path by default.

⚠️The project is currently in a rapid iteration phase, before each update, please pull the latest model from the remote side!

bash
# Download lightweight resource files for mobile device
bash command/download_models_general.sh Pikachu
# Download resource files for mobile device or PC/server
bash command/download_models_general.sh Megatron
# Download resource files for RV1109
bash command/download_models_general.sh Gundam_RV1109
# Download resource files for RV1106
bash command/download_models_general.sh Gundam_RV1106
# Download resource files for RK356X
bash command/download_models_general.sh Gundam_RK356X
# Download resource files for RK3588
bash command/download_models_general.sh Gundam_RK3588
# Download resource files for NVIDIA-GPU Device(TensorRT)
bash command/download_models_general.sh Megatron_TRT

# Download all model files
bash command/download_models_general.sh

Installing MNN

The '3rdparty' directory already includes the MNN library and specifies a particular version as the stable version. If you need to enable or disable additional configuration options during compilation, you can refer to the CMake Options provided by MNN. If you need to use your own precompiled version, feel free to replace it.

Requirements

CMake (version 3.20 or higher)
NDK (version 16 or higher, only required for Android) [Optional]
MNN (version 3.x or higher)
C++ Compiler
- Either GCC or Clang can be used (macOS does not require additional installation as Xcode is included)
  - Recommended GCC version is 4.9 or higher
    - Note that in some distributions, GCC (GNU C Compiler) and G++ (GNU C++ Compiler) are installed separately.
    - For instance, on Ubuntu, you need to install both gcc and g++
  - Recommended Clang version is 3.9 or higher
- arm-linux-gnueabihf (for RV1109/RV1126) [Optional]
  - Prepare the cross-compilation toolchain in advance, such as gcc-arm-8.3-2019.03-x86_64-arm-linux-gnueabihf
CUDA (version 11.x or higher) [Optional]
- GPU-based inference requires installing NVIDIA's CUDA dependencies on the device.
TensorRT (version 10 or higher) [Optional]
Eigen3
RKNN [Optional]
- Adjust and select versions currently supported for specific requirements.

Compilation

CMake option are used to control the various details of the compilation phase. Please select according to your actual requirements. CMake Option.

Local Compilation

If you are using macOS or Linux, you can quickly compile using the shell scripts provided in the command folder at the project root:

bash
cd InspireFace/
# Execute the local compilation script
bash command/build.sh

After compilation, you can find the local file in the build directory, which contains the compilation results. The install directory structure is as follows:

bash
inspireface-linux
   ├── include
   │   ├── herror.h
   │   ├── intypedef.h
   │   ├── inspireface.h
   │   ├── inspirecv/
   │   └── inspireface/
   └── lib
       └── libInspireFace.so

libInspireFace.so：Compiled dynamic linking library.
inspireface.h：Header file definition.
herror.h：Reference error number definition.
intypedef.h: Type definition file.
inspirecv: Simple cv library CPP header file folder.
inspireface: inspireface cpp header folder.

Cross Compilation

Cross compilation requires you to prepare the target platform's cross-compilation toolchain on the host machine in advance. Here, compiling for Rockchip's embedded devices RV1106 is used as an example:

bash
# Set the path for the cross-compilation toolchain
export ARM_CROSS_COMPILE_TOOLCHAIN=YOUR_DIR/arm-rockchip830-linux-uclibcgnueabihf
# Execute the cross-compilation script for RV1106
bash command/build_cross_rv1106_armhf_uclibc.sh

After the compilation is complete, you can find the compiled results in the build/inspireface-linux-armv7-rv1106-armhf-uclibc directory.

iOS Compilation

To compile for iOS, ensure you are using a Mac device. The script will automatically download third-party dependencies into the .macos_cache directory.

bash command/build_ios.sh

After the compilation is complete, inspireface.framework will be placed in the build/inspireface-ios directory.

Android Compilation

You can compile for Android using the following command, but first you need to set your Android NDK path:

export ANDROID_NDK=YOUR_ANDROID_NDK_PATH
bash command/build_android.sh

After the compilation is complete, arm64-v8a and armeabi-v7a libraries will be placed in the build/inspireface-android directory.

Linux-based NVIDIA GPU Acceleration with TensorRT Compilation

If you want to use NVIDIA GPU devices for accelerated inference on Linux, you need to install CUDA, cuDNN, and TensorRT-10 on your device, and configure the relevant environment variables.

bash
# Example, Change to your TensorRT-10 path
export TENSORRT_ROOT=/user/tunm/software/TensorRT-10

Before compiling, please ensure that your related environments such as CUDA and TensorRT-10 are available. If you encounter issues with finding CUDA libraries during the compilation process, you may need to check whether the relevant environment variables have been configured: CUDA_TOOLKIT_ROOT_DIR, CUDA_CUDART_LIBRARY.

bash
bash command/build_linux_tensorrt.sh

Additionally, you can use NVIDIA's Docker images for compilation. For example, to compile using a CUDA 12 and TensorRT-10 image on Ubuntu 22.04, you can execute the following commands:

bash
docker-compose up build-tensorrt-cuda12-ubuntu22

If you want to use pre-compiled libraries, you can use FindTensorRT.cmake to create links to CUDA and TensorRT.

React Native

For Android and iOS, in addition to the native interface, you can use the React Native library powered by Nitro Modules and JSI—providing ultra-fast, seamless bindings to the InspireFace SDK. For more details, check out the react-native-nitro-inspire-face repository or the documentation. Author: ronickg.

Supported Platforms and Architectures

We have completed the adaptation and testing of the software across various operating systems and CPU architectures. This includes compatibility verification for platforms such as Linux, macOS, iOS, and Android, as well as testing for specific hardware support to ensure stable operation in diverse environments.

No.	Platform	Architecture<sup><br/>(CPU)	Device<sup><br/>(Special)	Supported	Passed Tests	Release<sup><br/>(Online)
1	Linux<sup><br/>(CPU)	ARMv7	-
2		ARMv8	-
3		x86/x86_64	-
4	Linux<sup><br/>(Rockchip)	ARMv7	RV1109/RV1126
5		ARMv7	RV1103/RV1106
6		ARMv8	RK3566/RK3568
7		ARMv8	RK3588		-
8	Linux<sup><br/>(MNN_CUDA)	x86/x86_64	NVIDIA-GPU			-
9	Linux<sup><br/>(CUDA)	x86/x86_64	NVIDIA-GPU
10	MacOS	Intel	CPU/Metal/ANE
11		Apple Silicon	-
12	iOS	ARM	CPU/Metal/ANE
13	Android	ARMv7	-
14		ARMv8	-
15		x86_64	-
16	Android<sup><br/>(Rockchip)	ARMv8	RK3566/RK3568
17		ARMv8	RK3588
18	HarmonyOS	ARMv8	-	-	-	-
19	Linux<sup><br/>(Jetson series)	ARMv8	Jetson series	-	-	-

Device: Some special device support, primarily focused on computing power devices.
Supported: The solution has been fully developed and successfully verified on offline devices.
Passed Tests: The feature has at least passed unit tests on offline devices.
Release: The solution is already supported and has been successfully compiled and released through GitHub Actions.

Multi-platform compilation using Docker

We offer a method for rapid multi-platform compilation using Docker, provided that Docker is installed beforehand, and the appropriate commands are executed:

Bash
# Build x86 Ubuntu18.04
docker-compose up build-ubuntu18

# Build armv7 cross-compile
docker-compose up build-cross-armv7-armhf

# Build armv7 with support RV1109RV1126 device NPU cross-complie
docker-compose up build-cross-rv1109rv1126-armhf

# Build armv7 with support RV1106 device NPU cross-complie
docker-compose up build-cross-rv1106-armhf-uclibc

# Build armv8 with support RK356x device NPU cross-complie
docker-compose up build-cross-rk356x-aarch64

# Build Android with support arm64-v8a and armeabi-v7a
docker-compose up build-cross-android

# Compile the tensorRT back-end based on CUDA12 and then Ubuntu22.04
docker-compose up build-tensorrt-cuda12-ubuntu22

# Build all
docker-compose up

Example

C/C++ Sample: Use the recommended CAPI Interface

To integrate InspireFace into a C/C++ project, you simply need to link the InspireFace library and include the appropriate header files(We recommend using the more compatible CAPI headers). Below is a basic example demonstrating face detection:

c
#include <inspireface.h>
#include <herror.h>

...
  
HResult ret;
// The resource file must be loaded before it can be used
ret = HFLaunchInspireFace(packPath);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Load Resource error: %d", ret);
    return ret;
}

// Enable the functions in the pipeline: mask detection, live detection, and face quality
// detection
HOption option = HF_ENABLE_QUALITY | HF_ENABLE_MASK_DETECT | HF_ENABLE_LIVENESS;
// Non-video or frame sequence mode uses IMAGE-MODE, which is always face detection without
// tracking
HFDetectMode detMode = HF_DETECT_MODE_ALWAYS_DETECT;
// Maximum number of faces detected
HInt32 maxDetectNum = 20;
// Face detection image input level
HInt32 detectPixelLevel = 160;
// Handle of the current face SDK algorithm context
HFSession session = {0};
ret = HFCreateInspireFaceSessionOptional(option, detMode, maxDetectNum, detectPixelLevel, -1, &session);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Create FaceContext error: %d", ret);
    return ret;
}

// Configure some detection parameters
HFSessionSetTrackPreviewSize(session, detectPixelLevel);
HFSessionSetFilterMinimumFacePixelSize(session, 4);

// Load a image
HFImageBitmap image;
ret = HFCreateImageBitmapFromFilePath(sourcePath, 3, &image);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "The source entered is not a picture or read error.");
    return ret;
}
// Prepare an image parameter structure for configuration
HFImageStream imageHandle = {0};
ret = HFCreateImageStreamFromImageBitmap(image, rotation_enum, &imageHandle);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Create ImageStream error: %d", ret);
    return ret;
}

// Execute HF_FaceContextRunFaceTrack captures face information in an image
HFMultipleFaceData multipleFaceData = {0};
ret = HFExecuteFaceTrack(session, imageHandle, &multipleFaceData);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Execute HFExecuteFaceTrack error: %d", ret);
    return ret;
}

// Print the number of faces detected
auto faceNum = multipleFaceData.detectedNum;
HFLogPrint(HF_LOG_INFO, "Num of face: %d", faceNum);

// The memory must be freed at the end of the program
ret = HFReleaseImageBitmap(image);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Release image bitmap error: %d", ret);
    return ret;
}

ret = HFReleaseImageStream(imageHandle);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Release image stream error: %d", ret);
}

ret = HFReleaseInspireFaceSession(session);
if (ret != HSUCCEED) {
    HFLogPrint(HF_LOG_ERROR, "Release session error: %d", ret);
    return ret;
}

...

For more examples, you can refer to the cpp/sample sub-project located in the root directory. You can compile these sample executables by enabling the ISF_BUILD_WITH_SAMPLE option during the compilation process.

Note: For each error code feedback, you can click on this link to view detailed explanations.

C++ Sample: Use the C++ version of the header files

If you want to use C++ header files, then you need to enable ISF_INSTALL_CPP_HEADER during compilation. Executing the install command will add the C++ header files.

c
#include <iostream>
#include <memory>
#include <inspireface/inspireface.hpp>

...

// Set log level to info
INSPIRE_SET_LOG_LEVEL(inspire::LogLevel::ISF_LOG_INFO);

int32_t ret = 0;
// Global init(you only need to call once)
ret = INSPIREFACE_CONTEXT->Load("Pikachu");
INSPIREFACE_CHECK_MSG(ret == HSUCCEED, "Load model failed");

// Create face algorithm session
inspire::ContextCustomParameter custom_param;
custom_param.enable_recognition = true;
auto max_detect_face = 5;
auto detect_level_px = 320; // 160, 320, 640

// Create a face algorithm session
std::shared_ptr<inspire::Session> session(
    inspire::Session::CreatePtr(inspire::DETECT_MODE_ALWAYS_DETECT, max_detect_face, custom_param, detect_level_px));

// Load image(default format is BGR)
inspirecv::Image image = inspirecv::Image::Create("face.jpg");

// Create frame process
inspirecv::FrameProcess process =
    inspirecv::FrameProcess::Create(image, inspirecv::BGR, inspirecv::ROTATION_0);

// Detect face
std::vector<inspire::FaceTrackWrap> detect_results;
ret = session->FaceDetectAndTrack(process, detect_results);
INSPIRE_LOGI("Number of faces detected: %d", detect_results.size());
if (detect_results.size() == 0)
{
    INSPIRE_LOGW("No face detected");
    return -1;
}

// Copy image
inspirecv::Image image_copy = image.Clone();
// Draw face
auto thickness = 2;
for (auto &face : detect_results)
{
    auto rect = session->GetFaceBoundingBox(face);
    auto lmk = session->GetNumOfFaceDenseLandmark(face);
    image_copy.DrawRect(rect, inspirecv::Color::Red, thickness);
    for (auto &point : lmk)
    {
        image_copy.DrawCircle(point.As<int>(), 0, inspirecv::Color::Orange, thickness);
    }
}
// Save draw image
image_copy.Write("result.jpg");

// Face Embedding extract
inspire::FaceEmbedding face_embedding;
// Extract the first face feature
ret = session->FaceFeatureExtract(process, detect_results[0], face_embedding);
INSPIRE_LOGI("Length of face embedding: %d", face_embedding.embedding.size());

...

Please note that the C++ interface has not been fully tested. It is recommended to use the CAPI interface as the primary option.

More detailed cases:

Python Native Sample

The Python implementation is compiled based on InspireFace source code, and is integrated using a native interface approach.

Use pip to install InspireFace

You can use pip to install the InspireFace Python package:

bash
pip install inspireface

Python Native Sample

We provide a Python API that allows for more efficient use of the InspireFace library. After compiling the dynamic link library, you need to either symlink or copy it to the python/inspireface/modules/core directory within the root directory. You can then start testing by navigating to the python directory. Your Python environment will need to have some dependencies installed:

python >= 3.7
opencv-python
loguru
tqdm
numpy
ctypes

bash
# Use a symbolic link
ln -s YOUR_BUILD_DIR/install/InspireFace/lib/libInspireFace.so python/inspireface/modules/core/PLATFORM/ARCH/
# Navigate to the sub-project directory
cd python

Import inspireface for a quick facial detection example:

python
import cv2
import inspireface as isf

# Step 1: Initialize the SDK globally (only needs to be called once per application)
ret = isf.reload()
assert ret, "Launch failure. Please ensure the resource path is correct."

# Optional features, loaded during session creation based on the modules specified.
opt = isf.HF_ENABLE_FACE_POSE
session = isf.InspireFaceSession(opt, isf.HF_DETECT_MODE_ALWAYS_DETECT)

# Load the image using OpenCV.
image = cv2.imread(image_path)
assert image is not None, "Please check that the image path is correct."

# Perform face detection on the image.
faces = session.face_detection(image)
print(f"face detection: {len(faces)} found")

# Copy the image for drawing the bounding boxes.
draw = image.copy()
for idx, face in enumerate(faces):
    print(f"{'==' * 20}")
    print(f"idx: {idx}")
    # Print Euler angles of the face.
    print(f"roll: {face.roll}, yaw: {face.yaw}, pitch: {face.pitch}")
    # Draw bounding box around the detected face.
    x1, y1, x2, y2 = face.location
    cv2.rectangle(draw, (x1, y1), (x2, y2), (0, 0, 255), 2)

In the project, more usage examples are provided:

sample_face_detection.py: Facial detection example
sample_face_recognition.py: Facial recognition example
sample_face_track_from_video.py: Facial tracking from video stream example

Java and Android platform API

We have an Android SDK project that integrates pre-compiled dynamic libraries, and you can use it directly.

Precompiled library support:

arm64-v8a
armeabi-v7a
x86_64

a. Quick to use in Android

We released InspireFace's Android SDK on JitPack, which you can incorporate into your android projects in the following ways.

Step 1. Add the JitPack repository to your build file add it in your root build.gradle at the end of repositories:

groovy
allprojects {
    repositories {
       ...
       maven { url 'https://jitpack.io' }
    }
}

Step 2. Add the dependency

groovy
dependencies {
    implementation 'com.github.HyperInspire:inspireface-android-sdk:1.2.3.post4'
}

b. Use the Android example project

We have prepared an Android SDK project. You can download library from the Release Page or compile the Android library yourself and place it in the inspireface/libs directory of the Android sample project. You can compile and run this project using Android Studio.

bash
inspireface-android-sdk/inspireface/libs
├── arm64-v8a
│   └── libInspireFace.so
└── armeabi-v7a
    └── libInspireFace.so

You need to get the resource file from the release Release Page and place it in the asset/inspireface in your android project:

asset/
└── inspireface/
    │── Pikachu
    │── Megatron
    │── Gundam_RK356X
    └── Gundam_RK3588

How to use the Android/Java API

We provide a Java API for Android devices, which is implemented using Java Native Interface(JNI).

java
// Launch InspireFace, only need to call once
boolean launchStatus = InspireFace.GlobalLaunch(this, InspireFace.PIKACHU);
if (!launchStatus) {
    Log.e(TAG, "Failed to launch InspireFace");
}

// Create a ImageStream
ImageStream stream = InspireFace.CreateImageStreamFromBitmap(img, InspireFace.CAMERA_ROTATION_0);

// Create a session
CustomParameter parameter = InspireFace.CreateCustomParameter()
                .enableRecognition(true)
                .enableFaceQuality(true)
                .enableFaceAttribute(true)
                .enableInteractionLiveness(true)
                .enableLiveness(true)
                .enableMaskDetect(true);
Session session = InspireFace.CreateSession(parameter, InspireFace.DETECT_MODE_ALWAYS_DETECT, 10, -1, -1);

// Execute face detection
MultipleFaceData multipleFaceData = InspireFace.ExecuteFaceTrack(session, stream);
if (multipleFaceData.detectedNum > 0) {
    // Get face feature
    FaceFeature feature = InspireFace.ExtractFaceFeature(session, stream, multipleFaceData.tokens[0]);
    // ....
}

// .... 

// Release resource
InspireFace.ReleaseSession(session);
InspireFace.ReleaseImageStream(stream);

// Global release
InspireFace.GlobalRelease();

Test

In the project, there is a subproject called cpp/test. To compile it, you need to enable the ISF_BUILD_WITH_TEST switch, which will allow you to compile executable programs for testing.

bash
cmake -DISF_BUILD_WITH_TEST=ON ..

To run the test modules in the project, first check if the resource files exist in the test_res/pack directory. If they don't exist, you can either execute command/download_models_general.sh to download the required files, or download the files from the Release Page and manually place them in this directory.

bash

test_res
├── data
├── images
├── pack	<-- The model package files are here
├── save
├── valid_lfw_funneled.txt
├── video
└── video_frames

After compilation, you can find the executable program "Test" in YOUR_BUILD_FOLDER/test. The program accepts two optional parameters:

test_dir：Path to the test resource files
pack：Name of the model to be tested

bash
./Test --test_dir PATH/test_res --pack Pikachu

During the process of building the test program using CMake, it will involve selecting CMake parameters. For specific details, you can refer to the parameter configuration table.

Note: If you want to view the benchmark test report, you can click on the link.

Quick Test

If you need to perform a quick test, you can use the script we provide. This script will automatically download the test file test_res and build the test program to run the test.

Note: If you need to enable more comprehensive tests, you can adjust the options in the script as needed.

bash
# If you are using Ubuntu, you can execute this.
bash ci/quick_test_linux_x86_usual.sh

# If you are using another system (including Ubuntu), you can execute this.
bash ci/quick_test_local.sh

Every time code is committed, tests are run on GitHub Actions.

Features

The following Features and technologies are currently supported.

Feature	RKNPU<sup><br/>(RV1109/1126)	RKNPU<sup><br/>(RV1103/1106)	RKNPU<sup><br/>(RK3566/3568/3588)	ANE<sup><br/>(MacOS/iOS)	GPU<sup><br/>(TensorRT)
Face Detection
Landmark
Face Embeddings				-
Face Comparison	-	-	-	-	-
Face Recognition				-
Alignment	-	-	-	-	-
Tracking
Mask Detection	-			-	-
Silent Liveness	-			-	-
Face Quality
Pose Estimation
Face Attribute	-			-
Cooperative Liveness
Face Emotion<sup>New</sup>				-
Embedding Management	-	-	-	-	-

Some models and features that do not support NPU or GPU will automatically use CPU for computation when running the program.

Resource Package List

For different scenarios, we currently provide several Packs, each containing multiple models and configurations.

Name	Supported Devices	Note	Last Update	Link
Pikachu	CPU	Lightweight edge-side models	Jun 22, 2025	Download
Megatron	CPU, GPU	Mobile and server models	Jun 15, 2025	Download
Megatron_TRT	GPU	CUDA-based server models	Jun 15, 2025	Download
Gundam-RV1109	RKNPU	Supports RK1109 and RK1126	Jun 15, 2025	Download
Gundam-RV1106	RKNPU	Supports RV1103 and RV1106	Jul 6, 2025	Download
Gundam-RK356X	RKNPU	Supports RK3566 and RK3568	Jun 15, 2025	Download
Gundam-RK3588	RKNPU	Supports RK3588	Jun 15, 2025	Download