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yolov6

Table of contents

1. Description

This model used for object detection.

The model used in this example comes from the following open source projects:

https://github.com/airockchip/yolov6

2. Current Support Platform

RK3566, RK3568, RK3588, RK3562, RK1808, RV1109, RV1126

3. Pretrained Model

Download link:

./yolov6n.onnx
./yolov6s.onnx
./yolov6m.onnx

Download with shell command:

cd model
./download_model.sh

Note: The model provided here is an optimized model, which is different from the official original model. Take yolov6n.onnx as an example to show the difference between them.

  1. The comparison of their output information is as follows. The left is the official original model, and the right is the optimized model. As shown in the figure, the original one output is split into three groups. For example, in the set of outputs ([1,4,80,80],[1,80,80,80],[1,1,80,80]), [1,4,80,80] is the coordinate of the box, [1,80,80,80] is the confidence of the box corresponding to the 80 categories, and [1,1,80,80] is the sum of the confidence of the 80 categories.
Image
  1. Taking the the set of outputs ([1,4,80,80],[1,80,80,80],[1,1,80,80]) as an example, we remove the subgraphs behind the two convolution nodes in the model (the framed part in the figure), keep the outputs of these two convolutions ([1,4,80,80],[1,80,80,80]), and add a reducesum+clip branch for calculating the sum of the confidence of the 80 categories ([1,1,80,80]).
Image

4. Convert to RKNN

Usage:

cd python
python convert.py <onnx_model> <TARGET_PLATFORM> <dtype(optional)> <output_rknn_path(optional)>

# such as: 
python convert.py ../model/yolov6n.onnx rk3588
# output model will be saved as ../model/yolov6.rknn

Description:

  • <onnx_model>: Specify ONNX model path.
  • <TARGET_PLATFORM>: Specify NPU platform name. Support Platform refer [here](#2 Current Support Platform).
  • <dtype>(optional): Specify as i8, u8 or fp. i8/u8 for doing quantization, fp for no quantization. Default is i8.
  • <output_rknn_path>(optional): Specify save path for the RKNN model, default save in the same directory as ONNX model with name yolov6.rknn

5. Python Demo

Usage:

cd python
# Inference with PyTorch model or ONNX model
python yolov6.py --model_path <pt_model/onnx_model> --img_show

# Inference with RKNN model
python yolov6.py --model_path <rknn_model> --target <TARGET_PLATFORM> --img_show

Description:

  • <TARGET_PLATFORM>: Specify NPU platform name. Support Platform refer [here](#2 Current Support Platform).

  • <pt_model / onnx_model / rknn_model>: Specify the model path.

6. Android Demo

Note: RK1808, RV1109, RV1126 does not support Android.

6.1 Compile and Build

Please refer to the Compilation_Environment_Setup_Guide document to setup a cross-compilation environment and complete the compilation of C/C++ Demo.
Note: Please replace the model name with yolov6.

6.2 Push demo files to device

With device connected via USB port, push demo files to devices:

adb root
adb remount
adb push install/<TARGET_PLATFORM>_android_<ARCH>/rknn_yolov6_demo/ /data/

6.3 Run demo

adb shell
cd /data/rknn_yolov6_demo

export LD_LIBRARY_PATH=./lib
./rknn_yolov6_demo model/yolov6.rknn model/bus.jpg

  • After running, the result was saved as out.png. To check the result on host PC, pull back result referring to the following command:

    adb pull /data/rknn_yolov6_demo/out.png

  • Output result refer Expected Results.

7. Linux Demo

7.1 Compile and Build

Please refer to the Compilation_Environment_Setup_Guide document to setup a cross-compilation environment and complete the compilation of C/C++ Demo.
Note: Please replace the model name with yolov6.

7.2 Push demo files to device

  • If device connected via USB port, push demo files to devices:
adb push install/<TARGET_PLATFORM>_linux_<ARCH>/rknn_yolov6_demo/ /userdata/
  • For other boards, use scp or other approaches to push all files under install/<TARGET_PLATFORM>_linux_<ARCH>/rknn_yolov6_demo/ to userdata.

7.3 Run demo

adb shell
cd /userdata/rknn_yolov6_demo

export LD_LIBRARY_PATH=./lib
./rknn_yolov6_demo model/yolov6.rknn model/bus.jpg

  • After running, the result was saved as out.png. To check the result on host PC, pull back result referring to the following command:

    adb pull /userdata/rknn_yolov6_demo/out.png

  • Output result refer Expected Results.

8. Expected Results

This example will print the labels and corresponding scores of the test image detect results, as follows:

bus @ (97 137 553 437) 0.949
person @ (109 236 222 535) 0.938
person @ (212 239 286 511) 0.934
person @ (479 230 561 522) 0.919
person @ (79 325 119 516) 0.456
stop sign @ (80 150 99 192) 0.357
tie @ (160 282 169 299) 0.258

  • Note: Different platforms, different versions of tools and drivers may have slightly different results.