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# 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** toolchain dependency: **gcc-arm-10.3-2021.07-x86_64-arm-none-linux-gnueabihf**, download link: https://developer.arm.com/tools-and-software/open-source-software/developer-tools/gnu-toolchain/gnu-a/downloads/
# Support List
| Category | Model_name | Dtype | Platform |
| ---------------------- | ------------------------------------------------------------ | ------ | ------------- |
| Classification | [mobilenet_v2](https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz) | INT8 | A311D2/S905X5 |
| Classification | [resnet50-v2](https://github.com/onnx/models/blob/8e893eb39b131f6d3970be6ebd525327d3df34ea/vision/classification/resnet/model/resnet50-v2-7.onnx) | INT8 | A311D2/S905X5 |
| Object Detection | [yolov8](https://github.com/ultralytics/ultralytics) | INT8 | A311D2/S905X5 |
| Object Detection | [yolov11](https://github.com/ultralytics/ultralytics) | INT8 | A311D2/S905X5 |
| Object Detection | [yoloworld](https://github.com/AILab-CVC/YOLO-World) | INT8 | A311D2/S905X5 |
| Object Detection | [yoloe](https://github.com/ultralytics/ultralytics) | INT8 | A311D2/S905X5 |
| Object Detection | [yolox](https://github.com/Megvii-BaseDetection/YOLOX/) | INT8 | A311D2/S905X5 |
| Face Key Points | [retinaface](https://github.com/biubug6/Pytorch_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](https://github.com/TaoRuijie/ECAPA-TDNN) | Hybrid | A311D2/S905X5 |
| Speech Recognition | whisper | Hybrid | A311D2/S905X5 |
| Image-Text Matching | [clip](https://huggingface.co/openai/clip-vit-base-patch32) | Hybrid | A311D2/S905X5 |
| Chat LLM | deepseek | Hybrid | A311D2/S905X5 |
pre-build models:
[huggingface](https://huggingface.co/Amlogic-NN/amlnn-adla-models/tree/main)
# 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 SDK Setup
The C++ demos depend on the **AMLNN nnsdk** runtime library. The build system resolves it using the following priority order:
**Priority 1 – Environment variable (recommended)**
```bash
export AMLNN_HOME=/path/to/amlnn-toolkit
```
**Priority 2 – CMake auto-find (`FindAMLNN.cmake`)**
Handled automatically via `cmake/FindAMLNN.cmake` in the project root — useful in IDE and CI environments.
**Priority 3 – Sibling directory fallback**
If `AMLNN_HOME` is not set, the script 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:
```bash
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.
To build **all examples at once**, use the top-level batch script:
```bash
cd examples
./build-android-all.sh # auto-detects amlnn-toolkit (Priority 3)
# or explicitly:
AMLNN_HOME=/path/to/amlnn-toolkit ./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.
# **Release Notes**
| Version | Description |
| ------- | ------------- |
| 1.0.0 | First Version |
​
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