# rknn_model_zoo

**Repository Path**: polarbird/rknn_model_zoo

## Basic Information

- **Project Name**: rknn_model_zoo
- **Description**: https://github.com/airockchip/rknn_model_zoo 镜像
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 2
- **Forks**: 1
- **Created**: 2024-08-01
- **Last Updated**: 2025-06-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

[简体中文](README_CN.md) | [English](README.md)



# RKNN Model Zoo

## Description

`RKNN Model Zoo` is developed based on the RKNPU SDK toolchain and provides deployment examples for current mainstream algorithms. Include the process of `exporting the RKNN model` and using `Python API` and `CAPI` to infer the RKNN model.

- Support `RK3562`, `RK3566`, `RK3568`, `RK3588` ,  `RK3576`  platforms. 
- Limited support `RV1103`, `RV1106` 
- Support `RK1808`,  `RV1109`, `RV1126` platforms.



## Dependency library installation

`RKNN Model Zoo` relies on `RKNN-Toolkit2` for model conversion. The Android compilation tool chain is required when compiling the Android demo, and the Linux compilation tool chain is required when compiling the Linux demo. For the installation of these dependencies, please refer to the `Quick Start` documentation at https://github.com/airockchip/rknn-toolkit2/tree/master/doc.

- Please note that the Android compilation tool chain recommends using `version r18 or r19`. Using other versions may encounter the problem of Cdemo compilation failure.



## Model support

In addition to exporting the model from the corresponding respository, the models file are available on https://console.zbox.filez.com/l/8ufwtG (key: rknn). 

| Demo<br />                                                | Algorithm Category         | Dtype support | Pretrain model<br />                                         |
| --------------------------------------------------------- | -------------------------- | ------------- | ------------------------------------------------------------ |
| [mobilenet](./examples/mobilenet/README.md)               | Classification             | FP16/INT8     | [mobilenetv2-12.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/MobileNet/mobilenetv2-12.onnx) |
| [resnet](./examples/resnet/README.md)                     | Classification             | FP16/INT8     | [resnet50-v2-7.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/ResNet/resnet50-v2-7.onnx) |
| [yolov5](./examples/yolov5/README.md)                     | Object detection           | FP16/INT8     | [yolov5n.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5/yolov5n.onnx)<br />[yolov5s_relu.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5/yolov5s_relu.onnx)<br />[yolov5s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5/yolov5s.onnx)<br />[yolov5m.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5/yolov5m.onnx) |
| [yolov6](./examples/yolov6/README.md)                     | Object detection           | FP16/INT8     | [yolov6n.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov6/yolov6n.onnx)<br />[yolov6s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov6/yolov6s.onnx)<br />[yolov6m.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov6/yolov6m.onnx) |
| [yolov7](./examples/yolov7/README.md)                     | Object detection           | FP16/INT8     | [yolov7-tiny.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov7/yolov7-tiny.onnx)<br />[yolov7.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov7/yolov7.onnx) |
| [yolov8](./examples/yolov8/README.md)                     | Object detection           | FP16/INT8     | [yolov8n.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov8/yolov8n.onnx)<br />[yolov8s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov8/yolov8s.onnx)<br />[yolov8m.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov8/yolov8m.onnx) |
| [yolox](./examples/yolox/README.md)                       | Object detection           | FP16/INT8     | [yolox_s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolox/yolox_s.onnx)<br />[yolox_m.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolox/yolox_m.onnx) |
| [ppyoloe](./examples/ppyoloe/README.md)                   | Object detection           | FP16/INT8     | [ppyoloe_s.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/ppyoloe/ppyoloe_s.onnx)<br />[ppyoloe_m.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/ppyoloe/ppyoloe_m.onnx) |
| [deeplabv3](./examples/deeplabv3/README.md)               | Image segmentation         | FP16/INT8     | [deeplab-v3-plus-mobilenet-v2.pb](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/Deeplabv3/deeplab-v3-plus-mobilenet-v2.pb) |
| [yolov5-seg](./examples/yolov5_seg/README.md)             | Image segmentation         | FP16/INT8     | [yolov5n-seg.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5_seg/yolov5n-seg.onnx)<br />[yolov5s-seg.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5_seg/yolov5s-seg.onnx)<br />[yolov5m-seg.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov5_seg/yolov5m-seg.onnx) |
| [yolov8-seg](./examples/yolov8_seg/README.md)             | Image segmentation         | FP16/INT8     | [yolov8n-seg.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov8_seg/yolov8n-seg.onnx)<br />[yolov8s-seg.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov8_seg/yolov8s-seg.onnx)<br />[yolov8m-seg.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/yolov8_seg/yolov8m-seg.onnx) |
| [ppseg](./examples/ppseg/README.md)                       | Image segmentation         | FP16          | [pp_liteseg_cityscapes.onnx](https://ftzr.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/ppseg/pp_liteseg_cityscapes.onnx ) |
| [RetinaFace](./examples/RetinaFace/README.md)             | Face key points            | INT8          | [RetinaFace_mobile320.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/RetinaFace/RetinaFace_mobile320.onnx)<br />[RetinaFace_resnet50_320.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/RetinaFace/RetinaFace_resnet50_320.onnx) |
| [LPRNet](./examples/LPRNet/README.md)                     | Car Plate Recognition      | FP16/INT8     | [lprnet.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/LPRNet/lprnet.onnx) |
| [PPOCR-Det](./examples/PPOCR/PPOCR-Det/README.md)         | Text detection             | FP16/INT8     | [ppocrv4_det.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/PPOCR/ppocrv4_det.onnx) |
| [PPOCR-Rec](./examples/PPOCR/PPOCR-Rec/README.md)         | Text recognition           | FP16          | [ppocrv4_rec.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/PPOCR/ppocrv4_rec.onnx) |
| [lite_transformer](./examples/lite_transformer/README.md) | Neural Machine Translation | FP16          | [lite-transformer-encoder-16.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/lite_transformer/lite-transformer-encoder-16.onnx)<br />[lite-transformer-decoder-16.onnx](https://ftrg.zbox.filez.com/v2/delivery/data/95f00b0fc900458ba134f8b180b3f7a1/examples/lite_transformer/lite-transformer-decoder-16.onnx) |





## Model performance benchmark(FPS)

| demo             | model_name                   | inputs_shape            | dtype | RK3566 RK3568 | RK3562 | RK3588 @single_core | RK3576 @single_core | RK3576 <br />@single_core @sparse_weight | RV1109 | RV1126 | RK1808 |
| ---------------- | ---------------------------- | ----------------------- | ----- | ------------- | ------ | ------------------- | ------------------- | ---------------------------------------- | ------ | ------ | ------ |
| mobilenet        | mobilenetv2-12               | [1, 3, 224, 224]        | INT8  | 197.4         | 266.8  | 433.0               | 452.3               | 483.9                                    | 213.5  | 316.5  | 168.6  |
| resnet           | resnet50-v2-7                | [1, 3, 224, 224]        | INT8  | 40.6          | 54.5   | 108.6               | 97.4                | 129.9                                    | 24.5   | 36.4   | 37.0   |
| yolov5           | yolov5s_relu                 | [1, 3, 640, 640]        | INT8  | 26.7          | 31.6   | 63.3                | 62.6                | 82.0                                     | 20.3   | 29.3   | 36.7   |
|                  | yolov5n                      | [1, 3, 640, 640]        | INT8  | 41.6          | 43.8   | 68.1                | 104.4               | 112.2                                    | 36.4   | 53.5   | 61.0   |
|                  | yolov5s                      | [1, 3, 640, 640]        | INT8  | 19.9          | 22.7   | 42.5                | 54.2                | 65.5                                     | 13.7   | 20.1   | 28.1   |
|                  | yolov5m                      | [1, 3, 640, 640]        | INT8  | 8.7           | 10.6   | 19.3                | 23.0                | 31.5                                     | 5.8    | 8.5    | 13.1   |
| yolov6           | yolov6n                      | [1, 3, 640, 640]        | INT8  | 50.2          | 51.5   | 93.8                | 98.6                | 136.6                                    | 37.7   | 56.8   | 66.4   |
|                  | yolov6s                      | [1, 3, 640, 640]        | INT8  | 15.2          | 16.8   | 34.1                | 33.1                | 55.3                                     | 10.9   | 16.4   | 24.0   |
|                  | yolov6m                      | [1, 3, 640, 640]        | INT8  | 7.5           | 8.0    | 17.6                | 17.0                | 27.8                                     | 5.7    | 8.3    | 11.4   |
| yolov7           | yolov7-tiny                  | [1, 3, 640, 640]        | INT8  | 29.9          | 34.9   | 69.7                | 70.9                | 91.8                                     | 15.6   | 22.5   | 37.2   |
|                  | yolov7                       | [1, 3, 640, 640]        | INT8  | 4.7           | 5.5    | 10.9                | 12.5                | 17.9                                     | 3.3    | 4.9    | 7.4    |
| yolov8           | yolov8n                      | [1, 3, 640, 640]        | INT8  | 35.7          | 38.5   | 59.6                | 79.5                | 95.6                                     | 24.1   | 36.0   | 41.9   |
|                  | yolov8s                      | [1, 3, 640, 640]        | INT8  | 15.4          | 17.1   | 32.8                | 38.7                | 52.4                                     | 9.0    | 13.2   | 19.1   |
|                  | yolov8m                      | [1, 3, 640, 640]        | INT8  | 6.6           | 7.5    | 14.8                | 15.9                | 23.5                                     | 3.9    | 5.8    | 9.1    |
| yolox            | yolox_s                      | [1, 3, 640, 640]        | INT8  | 15.5          | 17.7   | 32.9                | 36.4                | 46.7                                     | 10.6   | 15.7   | 22.9   |
|                  | yolox_m                      | [1, 3, 640, 640]        | INT8  | 6.7           | 8.1    | 14.8                | 16.5                | 23.2                                     | 4.7    | 6.8    | 10.5   |
| ppyoloe          | ppyoloe_s                    | [1, 3, 640, 640]        | INT8  | 17.5          | 19.7   | 32.9                | 30.0                | 34.4                                     | 11.3   | 16.4   | 21.0   |
|                  | ppyoloe_m                    | [1, 3, 640, 640]        | INT8  | 7.9           | 8.3    | 16.2                | 12.9                | 14.8                                     | 5.2    | 7.7    | 9.4    |
| deeplabv3        | deeplab-v3-plus-mobilenet-v2 | [1, 513, 513, 1]        | INT8  | 10.7          | 20.7   | 34.4                | 38.1                | 42.5                                     | 10.3   | 13.1   | 4.4    |
| yolov5_seg       | yolov5n-seg                  | [1, 3, 640, 640]        | INT8  | 33.9          | 36.3   | 58.0                | 82.4                | 92.2                                     | 28.7   | 41.9   | 49.6   |
|                  | yolov5s-seg                  | [1, 3, 640, 640]        | INT8  | 15.3          | 17.2   | 32.6                | 39.5                | 51.1                                     | 9.7    | 14.0   | 22.4   |
|                  | yolov5m-seg                  | [1, 3, 640, 640]        | INT8  | 6.8           | 8.1    | 15.2                | 17.2                | 25.1                                     | 4.7    | 6.9    | 10.7   |
| yolov8_seg       | yolov8n-seg                  | [1, 3, 640, 640]        | INT8  | 29.1          | 30.7   | 49.1                | 64.5                | 78.0                                     | 18.6   | 27.8   | 32.7   |
|                  | yolov8s-seg                  | [1, 3, 640, 640]        | INT8  | 11.8          | 11.3   | 25.4                | 29.3                | 39.7                                     | 6.7    | 9.8    | 14.5   |
|                  | yolov8m-seg                  | [1, 3, 640, 640]        | INT8  | 5.2           | 6.1    | 11.6                | 12.1                | 18.1                                     | 3.1    | 4.6    | 6.8    |
| ppseg            | ppseg_lite_1024x512          | [1, 3, 512, 512]        | INT8  | 2.6           | 4.6    | 13.0                | 8.7                 | 35.5                                     | 18.4   | 27.2   | 14.7   |
| RetinaFace       | RetinaFace_mobile320         | [1, 3, 320, 320]        | INT8  | 142.5         | 279.5  | 234.7               | 416.0               | 396.8                                    | 146.3  | 210.1  | 242.2  |
|                  | RetinaFace_resnet50_320      | [1, 3, 320, 320]        | INT8  | 18.5          | 26.0   | 48.8                | 47.3                | 70.4                                     | 14.7   | 20.9   | 24.2   |
| LPRNet           | lprnet                       | [1, 3, 24, 94]          | INT8  | 58.2          | 119.7  | 204.4               | 130.2               | 130.6                                    | 30.6   | 47.8   | 30.1   |
| PPOCR-Det        | ppocrv4_det                  | [1, 3, 480, 480]        | INT8  | 24.4          | 27.5   | 43.0                | 46.1                | 47.0                                     | 11.1   | 16.2   | 9.1    |
| PPOCR-Rec        | ppocrv4_rec                  | [1, 3, 48, 320]         | FP16  | 20.0          | 45.1   | 35.7                | 55                  | 58.9                                     | 1.0    | 1.6    | 6.7    |
| lite_transformer | lite-transformer-encoder-16  | embedding-256, token-16 | FP16  | 130.8         | 656.7  | 261.5               | 609.1               | 674.8                                    | 22.7   | 35.6   | 97.8   |
|                  | lite-transformer-decoder-16  | embedding-256, token-16 | FP16  | 114.3         | 151.3  | 164.0               | 240                 | 341.8                                    | 49.0   | 66.3   | 114.9  |

- This performance data are collected based on the maximum NPU frequency of each platform.
- This performance data calculate the time-consuming of model inference. Does not include the time-consuming of pre-processing and post-processing.
- RK3576 with sparse_weight referring to the performance when enabling the sparse weight for models 
- Note: Models with sparse weight (via Kernel) should have improved performance, but may have accuracy drops depending on models.



## Compile Demo

For Linux develop board:

```sh
./build-linux.sh -t <target> -a <arch> -d <build_demo_name> [-b <build_type>] [-m]
    -t : target (rk356x/rk3588/rk3576/rv1106/rk1808/rv1126)
    -a : arch (aarch64/armhf)
    -d : demo name
    -b : build_type(Debug/Release)
    -m : enable address sanitizer, build_type need set to Debug
Note: 'rk356x' represents rk3562/rk3566/rk3568, 'rv1106' represents rv1103/rv1106, 'rv1126' represents rv1109/rv1126

# Here is an example for compiling yolov5 demo for 64-bit Linux RK3566.
./build-linux.sh -t rk356x -a aarch64 -d yolov5
```

For Android development board:

```sh
# For Android develop boards, it's require to set path for Android NDK compilation tool path according to the user environment
export ANDROID_NDK_PATH=~/opts/ndk/android-ndk-r18b
./build-android.sh -t <target> -a <arch> -d <build_demo_name> [-b <build_type>] [-m]
    -t : target (rk356x/rk3588/rk3576)
    -a : arch (arm64-v8a/armeabi-v7a)
    -d : demo name
    -b : build_type (Debug/Release)
    -m : enable address sanitizer, build_type need set to Debug

# Here is an example for compiling yolov5 demo for 64-bit Android RK3566.
./build-android.sh -t rk356x -a arm64-v8a -d yolov5
```



## Release Notes

| Version | Description                                                  |
| ------- | ------------------------------------------------------------ |
| 2.0.0   | Add new support for `RK3576` for all demo.<br />Full support for `RK1808`,  `RK1109`, `RK1126` platform. |
| 1.6.0   | New demo release, including object detection, image segmentation, OCR, car plate detection&recognition etc.<br />Full support for `RK3566`, `RK3568`, `RK3588`, `RK3562` platforms.<br />Limited support for `RV1103`, `RV1106` platforms. |
| 1.5.0   | Yolo detection demo release.                                 |



## Environment dependencies

All demos in `RKNN Model Zoo` are verified based on the latest RKNPU SDK. If using a lower version for verification, the inference performance and inference results may be wrong.

| Version | RKNPU2 SDK | RKNPU1 SDK |
| ------- | ---------- | ---------- |
| 2.0.0   | >=2.0.0    | >=1.7.5    |
| 1.6.0   | >=1.6.0    | -          |
| 1.5.0   | >=1.5.0    | >=1.7.3    |



## RKNPU Resource

- RKNPU2 SDK: https://github.com/airockchip/rknn-toolkit2
- RKNPU1 SDK: https://github.com/airockchip/rknn-toolkit



## License

[Apache License 2.0](./LICENSE)