go-rknnlite/example/yolov11

YOLOv11 Example

Usage

Make sure you have downloaded the data files first for the examples. You only need to do this once for all examples.

cd example/
git clone --depth=1 https://github.com/swdee/go-rknnlite-data.git data

Run the YOLOv11 example on rk3588 or replace with your Platform model.

cd example/yolov11
go run yolov11.go -p rk3588

This will result in the output of:

Driver Version: 0.9.6, API Version: 2.3.0 (c949ad889d@2024-11-07T11:35:33)
Model Input Number: 1, Ouput Number: 9
Input tensors:
  index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
Output tensors:
  index=0, name=462, n_dims=4, dims=[1, 64, 80, 80], n_elems=409600, size=409600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-60, scale=0.117048
  index=1, name=onnx::ReduceSum_476, n_dims=4, dims=[1, 80, 80, 80], n_elems=512000, size=512000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003011
  index=2, name=480, n_dims=4, dims=[1, 1, 80, 80], n_elems=6400, size=6400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003041
  index=3, name=487, n_dims=4, dims=[1, 64, 40, 40], n_elems=102400, size=102400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-37, scale=0.084344
  index=4, name=onnx::ReduceSum_501, n_dims=4, dims=[1, 80, 40, 40], n_elems=128000, size=128000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003592
  index=5, name=505, n_dims=4, dims=[1, 1, 40, 40], n_elems=1600, size=1600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
  index=6, name=512, n_dims=4, dims=[1, 64, 20, 20], n_elems=25600, size=25600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-44, scale=0.078218
  index=7, name=onnx::ReduceSum_526, n_dims=4, dims=[1, 80, 20, 20], n_elems=32000, size=32000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003861
  index=8, name=530, n_dims=4, dims=[1, 1, 20, 20], n_elems=400, size=400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003878
bus @ (93 134 552 437) 0.942072
person @ (476 231 559 522) 0.899602
person @ (108 237 225 537) 0.891880
person @ (211 240 283 508) 0.884158
person @ (79 326 126 518) 0.613891
Model first run speed: inference=48.848268ms, post processing=597.322µs, rendering=702.611µs, total time=50.148201ms
Saved object detection result to ../data/bus-yolov11-out.jpg
Benchmark time=3.980790684s, count=100, average total time=39.807906ms
done

The saved JPG image with object detection markers.

To use your own RKNN compiled model and images.

go run yolov11.go -m <RKNN model file> -i <image file> -l <labels txt file> -o <output jpg file> -p <platform>

The labels file should be a text file containing the labels the Model was trained on. It should have one label per line.

See the help for command line parameters.

$ go run yolov11.go --help

Usage of /tmp/go-build4280368118/b001/exe/yolov11:
  -i string
        Image file to run object detection on (default "../data/bus.jpg")
  -l string
        Text file containing model labels (default "../data/coco_80_labels_list.txt")
  -m string
        RKNN compiled YOLO model file (default "../data/models/rk3588/yolov11s-rk3588.rknn")
  -o string
        The output JPG file with object detection markers (default "../data/bus-yolov11-out.jpg")
  -p string
        Rockchip CPU Model number [rk3562|rk3566|rk3568|rk3576|rk3582|rk3582|rk3588] (default "rk3588")

Docker

To run the YOLOv11 example using the prebuilt docker image, make sure the data files have been downloaded first, then run.

# from project root directory

docker run --rm \
  --device /dev/dri:/dev/dri \
  -v "$(pwd):/go/src/app" \
  -v "$(pwd)/example/data:/go/src/data" \
  -v "/usr/include/rknn_api.h:/usr/include/rknn_api.h" \
  -v "/usr/lib/librknnrt.so:/usr/lib/librknnrt.so" \
  -w /go/src/app \
  swdee/go-rknnlite:latest \
  go run ./example/yolov11/yolov11.go -p rk3588

Proprietary Models

The example YOLOv11 model used has been trained on the COCO dataset so makes use of the default Post Processor setup. If you have trained your own Model and have set specific Classes or want to use alternative Box and NMS Threshold values, then initialize the postprocess.NewYOLOv11 with your own YOLOv11Params.

In the file postprocess/yolov11.go see function YOLOv11COCOParams for how to configure your own custom parameters.

Benchmarks

The following table shows a comparison of the benchmark results across the three distinct platforms.

Platform	Execution Time	Average Inference Time Per Image
rk3588	3.98s	39.80ms
rk3576	4.11s	41.19ms
rk3566	11.57s	115.73ms

Note that these examples are only using a single NPU core to run inference on. The results would be different when running a Pool of models using all NPU cores available. Secondly the Rock 4D (rk3576) has DDR5 memory versus the Rock 5B (rk3588) with slower DDR4 memory.

Background

This YOLOv11 example is a Go conversion of the C API example.