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Export ONNX Model

Check requirements

pip install onnx>=1.10.0

Export script

python ./deploy/ONNX/export_onnx.py \
    --weights yolov6s.pt \
    --img 640 \
    --batch 1 \
    --simplify

Description of all arguments

  • --weights : The path of yolov6 model weights.
  • --img : Image size of model inputs.
  • --batch : Batch size of model inputs.
  • --half : Whether to export half-precision model.
  • --inplace : Whether to set Detect() inplace.
  • --simplify : Whether to simplify onnx. Not support in end to end export.
  • --end2end : Whether to export end to end onnx model. Only support onnxruntime and TensorRT >= 8.0.0 .
  • --trt-version : Export onnx for TensorRT version. Support : 7 or 8.
  • --ort : Whether to export onnx for onnxruntime backend.
  • --with-preprocess : Whether to export preprocess with bgr2rgb and normalize (divide by 255)
  • --topk-all : Topk objects for every image.
  • --iou-thres : IoU threshold for NMS algorithm.
  • --conf-thres : Confidence threshold for NMS algorithm.
  • --device : Export device. Cuda device : 0 or 0,1,2,3 ... , CPU : cpu .

Download

End2End export

Now YOLOv6 supports end to end detect for onnxruntime and TensorRT !

If you want to deploy in TensorRT, make sure you have installed TensorRT !

onnxruntime backend

Usage

python ./deploy/ONNX/export_onnx.py \
    --weights yolov6s.pt \
    --img 640 \
    --batch 1 \
    --end2end \
    --ort

You will get an onnx with NonMaxSuppression operator .

TensorRT backend (TensorRT version == 7.2.3.4)

Usage

python ./deploy/ONNX/export_onnx.py \
    --weights yolov6s.pt \
    --img 640 \
    --batch 1 \
    --end2end \
    --trt-version 7

You will get an onnx with BatchedNMSDynamic_TRT plugin .

TensorRT backend (TensorRT version>= 8.0.0)

Usage

python ./deploy/ONNX/export_onnx.py \
    --weights yolov6s.pt \
    --img 640 \
    --batch 1 \
    --end2end \
    --trt-version 8

You will get an onnx with EfficientNMS_TRT plugin .

Outputs Description

The onnx outputs are as shown :

num_dets means the number of object in every image in its batch .

det_boxes means topk(100) object's location about [x0,y0,x1,y1] .

det_scores means the confidence score of every topk(100) objects .

det_classes means the category of every topk(100) objects .

You can export TensorRT engine use trtexec tools.

Usage

For both TensorRT-7 and TensorRT-8 trtexec tool is avaiable.

trtexec --onnx=yolov6s.onnx \
        --saveEngine=yolov6s.engine \
        --workspace=8192 # 8GB
        --fp16 # if export TensorRT fp16 model

Evaluate TensorRT model's performance

When we get the TensorRT model, we can evalute its performance by:

python deploy/ONNX/eval_trt.py --weights yolov6s.engine --batch-size=1 --data data/coco.yaml

Dynamic Batch Inference

YOLOv6 support dynamic batch export and inference, you can refer to:

export ONNX model with dynamic batch

export TensorRT model with dynamic batch