amlnn-model-playground

Introduction

​ amlnn-model-playground Has been developed using the amlnn toolkit, by completing the model conversion and deployment steps, we have created a ready to go model zoo for commonly used models. The demo package provides complete model conversion scripts, as well as a complete workflow for Python API, OpenAI API, and C API to run the converted model.

Objective: To help users get started and deploy models on the Amlogic NPU platform. The rich algorithm library in the model zoo can help guide developers through test, benchmarking, proof of concept and deployment of edge AI products.

Dependencies

• The model conversion functionality in amlnn-model-playground currently relies on the model conversion tool adla-toolkit-binary-x.x.x.x provided by Amlogic, please contact your sales representivie for access in the short term.
• Android compilation depends on the NDK toolchain. Currently, version r25c is recommended. Download link: https://github.com/android/ndk/wiki/Unsupported-Downloads
• Linux compilation for Yocto Toolchain, please contact your sales.

Support List

Category	Model_name	Dtype	Platform
Classification	mobilenet_v2	INT8	A311D2/S905X5
Classification	resnet50-v2	INT8	A311D2/S905X5
Object Detection	yolov8	INT8	A311D2/S905X5
Object Detection	yolov11	INT8	A311D2/S905X5
Object Detection	yoloworld	INT8	A311D2/S905X5
Object Detection	yoloe	INT8	A311D2/S905X5
Object Detection	yolox	INT8	A311D2/S905X5
Face Key Points	retinaface	INT8	A311D2/S905X5
Text Detection	ppocr-det	INT8	A311D2/S905X5
Pose Estimation	blazepose_detect	INT8	A311D2/S905X5
Pose Estimation	blazepose_landmark	INT8	A311D2/S905X5
Voiceprint recognition	ECAPA-TDNN	Hybrid	A311D2/S905X5
Speech Recognition	whisper	Hybrid	A311D2/S905X5
Image-Text Matching	clip	Hybrid	A311D2/S905X5
Chat LLM	deepseek	Hybrid	A311D2/S905X5

pre-build models: huggingface

Benchmark List(FPS)

Examples	Model_name	input_shapes	Dtype	S905X5	A311D2
mobilenet	mobilenet_v2	[1, 3, 224, 224]	INT8	1047.54	798.94
resnet	resnet50-v2	[1, 3, 224, 224]	INT8	106.78	128.91
yolov8	yolov8n	[1, 3, 640, 640]	INT8	101.72	95.14
	yolov8s	[1, 3, 640, 640]	INT8	42.33	42.77
	yolov8m	[1, 3, 640, 640]	INT8	19.67	19.82
	yolov8l	[1, 3, 640, 640]	INT8	10.53	10.12
yolov11	yolov11n	[1, 3, 640, 640]	INT8	41.14	41.48
yoloworld	yoloworld	[1, 3, 480, 640]	INT8	19.38	19.04
yoloe	yoloe	[1, 3, 288, 512]	INT8	53.9	37.8
yolox	yolox_tiny	[1, 3, 640, 640]	INT8	42.9	35.89
	yolox_s	[1, 3, 640, 640]	INT8	35.12	33.31
	yolox_m	[1, 3, 640, 640]	INT8	18.55	17.98
retinaface	retinaface	[1, 3, 320, 320]	INT8	341.99	305.89
ppocr-det	paddleocrv4-det	[1, 3, 640, 640]	INT8	37.66	38.85
blazepose_detect	blazepose_detection	[1, 3, 224, 224]	INT8	476.29	461.74
blazepose_landmark	blazepose_landmark_full	[1, 3, 256, 256]	INT16	84.59	70.31
Whisper	encoder_tiny_en	[1, 80, 3000]	Hybrid	0.71	0.58
	decoder_tiny_en	[1, 1500, 384]&[1, 48]	Hybrid	10.35	9.22
Clip	clip-vit-base-patch32	[1, 3, 224, 224]	Hybrid	7.48	6.82

• The performance data represents the runtime of the model on the NPU, as tested using the native case. Unless otherwise specified, it does not include the time spent on pre- and post-processing.
• means currently supported.

Examples Compile

AMLNN Runtime setup

The C++ demos depend on the AMLNN runtime library. The build system automatically looks for amlnn-toolkit as a sibling directory:

modelzoo/
├── amlnn-model-playground/   ← this repo
└── amlnn-toolkit/            ← SDK placed here automatically found

Clone it with:

git clone https://github.com/Amlogic-NN/amlnn-toolkit.git ../amlnn-toolkit

​ Each example directory contains a build-android.sh and build-linux.sh script. For compilation steps, refer to Chapter 4 of the README.md file in the corresponding example directory.

Android Complication

Android compilation requires the NDK toolchain. The build scripts look for the NDK path via the environment variables

Set environment variables before building, for example:

export ANDROID_NDK_PATH=/path/to/android-ndk-r25c

Note: NDK r25c is recommended. Download: https://github.com/android/ndk/wiki/Unsupported-Downloads

To build all examples at once, use the top-level batch script:

./examples/build-android-all.sh          # auto-detects amlnn-toolkit
# or explicitly:
AMLNN_HOME=/path/to/amlnn-toolkit ./build-android-all.sh

#clean build files
./examples/build-android-all.sh

The script automatically cleans the previous build, resolves the AMLNN SDK via the priority rules above, and prints a build summary at the end.

Yocto Compilation

Each example's build-linux.sh also supports Yocto mode via the -m yocto flag.

Dependency: A Yocto SDK (Poky). Set the path via environment variable or -s flag:

export YOCTO_SDK_ROOT=/path/to/poky/sdk

The toolchain file is shared across all demos at examples/cmake/yocto-toolchain.cmake.

Build all demos at once:

# 64-bit
./examples/build-linux-all.sh -m yocto -s /path/to/poky/sdk

# 32-bit
./examples/build-linux-all.sh -m yocto -b 32 -s /path/to/poky/32bit-sdk

# Clean yocto build artifacts
./examples/clean-linux-all.sh -m yocto

Note: The LLMs demo is automatically excluded from the batch build scripts.

Build a single demo:

# 64-bit (default)
./examples/yolox/cpp/build-linux.sh -m yocto -s /path/to/poky/sdk

# 32-bit
./examples/yolox/cpp/build-linux.sh -m yocto -b 32 -s /path/to/poky/32bit-sdk

Release Notes

Version	Description
1.0.0	First Version

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