yolov6/deploy/NCNN

• Export NCNN Model By PNNX with TorchScript
• • Export TorchScript
  • • • Description of all arguments
  • Export NCNN with TorchScript
  • Run inference with NCNN-Python
  • • • Description of all arguments
  • Download

Export NCNN Model By PNNX with TorchScript

Export TorchScript

python ./deploy/NCNN/export_torchscript.py \
  --weights yolov6lite_s.pt \
  --img 320 320 \
  --batch 1

Description of all arguments

• --weights : The path of yolov6 model weights.
• --img : Image size of model inputs.
• --batch : Batch size of model inputs.
• --device : Export device. Cuda device : 0 or 0,1,2,3 ... , CPU : cpu .

Export NCNN with TorchScript

• Download tools from PNNX

• Usage

  Unzip the pnnx-YYYYMMDD-PLANTFORM.zip and add the pnnx to your PATH .

  Then run the following command to export ncnn model :

  mkdir -p work_dir
  mv yolov6lite_s.torchscript work_dir
  cd work_dir
  pnnx yolov6lite_s.torchscript inputshape=[1,3,320,320]f32
  

  You will get yolov6lite_s.ncnn.bin and yolov6lite_s.ncnn.param in work_dir .

  If you want to try int8 quantization, you can get more information from here .

Run inference with NCNN-Python

python3 deploy/NCNN/infer-ncnn-model.py \
  data/images/image1.jpg \
  work_dir/yolov6lite_s.ncnn.param \
  work_dir/yolov6lite_s.ncnn.bin \
  --img-size 320 320 \
  --max-stride 64 \
  --show

Description of all arguments

• img : The path of image you want to detect.
• param : The NCNN param path.
• bin : The NCNN bin path.
• --show : Whether to show detection resulut.
• --out-dir : The output path to save detection result.
• --img-size : The image height and width for model input.
• --max-stride : The yolov6 lite model max stride.

Notice!

If you want to try norm yolov6 model such as yolov6n/s/m/l, you should add --max-stride 32 flags .

Download

• YOLOv6-lite-s
• YOLOv6-lite-m
• YOLOv6-lite-l
• YOLOv6-lite-l-320x192
• YOLOv6-lite-l-224x128
• YOLOv6-n
• YOLOv6-s
• YOLOv6-m
• YOLOv6-l