go-rknnlite/example/yolov10

YOLOv10 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 https://github.com/swdee/go-rknnlite-data.git data

Run the YOLOv10 example.

cd example/yolov10
go run yolov10.go

This will result in the output of:

Driver Version: 0.8.2, API Version: 1.6.0 (9a7b5d24c@2023-12-13T17:31:11)
Model Input Number: 1, Ouput Number: 6
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=502, n_dims=4, dims=[1, 64, 80, 80], n_elems=409600, size=409600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-62, scale=0.086849
  index=1, name=516, n_dims=4, dims=[1, 80, 80, 80], n_elems=512000, size=512000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.002931
  index=2, name=523, n_dims=4, dims=[1, 64, 40, 40], n_elems=102400, size=102400, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-55, scale=0.072764
  index=3, name=537, n_dims=4, dims=[1, 80, 40, 40], n_elems=128000, size=128000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003646
  index=4, name=544, n_dims=4, dims=[1, 64, 20, 20], n_elems=25600, size=25600, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-68, scale=0.058066
  index=5, name=558, n_dims=4, dims=[1, 80, 20, 20], n_elems=32000, size=32000, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003894
bus @ (92 136 555 436) 0.958003
person @ (110 235 226 536) 0.907376
person @ (212 240 285 509) 0.887905
person @ (476 233 559 521) 0.829490
Model first run speed: inference=58.666351ms, post processing=2.771067ms, rendering=1.236349ms, total time=62.673767ms
Saved object detection result to ../data/bus-yolov10-out.jpg
Benchmark time=4.144871338s, count=100, average total time=41.448713ms
done

The saved JPG image with object detection markers.

To use your own RKNN compiled model and images.

go run yolov10.go -m <RKNN model file> -i <image file> -l <labels txt file> -o <output jpg file>

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

Proprietary Models

The example YOLOv10 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.NewYOLOv10 with your own YOLOv10Params.

In the file postprocess/yolov10.go see function YOLOv10COCOParams for how to configure your own custom parameters.

Background

This YOLOv10 example is a Go conversion of the C API example.