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YOLOv6.md

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YOLOv6 usage

NOTE: You need to change the branch of the YOLOv6 repo according to the version of the model you want to convert.

NOTE: The yaml file is not required.

Convert model

1. Download the YOLOv6 repo and install the requirements

git clone https://github.com/meituan/YOLOv6.git
cd YOLOv6
pip3 install -r requirements.txt
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_yoloV6.py file from DeepStream-Yolo/utils directory to the YOLOv6 folder.

3. Download the model

Download the pt file from YOLOv6 releases (example for YOLOv6-S 4.0)

wget https://github.com/meituan/YOLOv6/releases/download/0.4.0/yolov6s.pt

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for YOLOv6-S 4.0)

python3 export_yoloV6.py -w yolov6s.pt --dynamic

NOTE: To convert a P6 model

--p6

NOTE: To change the inference size (defaut: 640 / 1280 for --p6 models)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model (DeepStream >= 6.0)

--simplify

NOTE: To use dynamic batch-size (DeepStream >= 6.1)

--dynamic

NOTE: To use static batch-size (example for batch-size = 4)

--batch 4

NOTE: If you are using the DeepStream 5.1, remove the --dynamic arg and use opset 12 or lower. The default opset is 13.

--opset 12

5. Copy generated file

Copy the generated ONNX model file to the DeepStream-Yolo folder.

Compile the lib

Open the DeepStream-Yolo folder and compile the lib

  • DeepStream 6.3 on x86 platform

    CUDA_VER=12.1 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 6.2 on x86 platform

    CUDA_VER=11.8 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 6.1.1 on x86 platform

    CUDA_VER=11.7 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 6.1 on x86 platform

    CUDA_VER=11.6 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 6.0.1 / 6.0 on x86 platform

    CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 5.1 on x86 platform

    CUDA_VER=11.1 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 6.3 / 6.2 / 6.1.1 / 6.1 on Jetson platform

    CUDA_VER=11.4 make -C nvdsinfer_custom_impl_Yolo

  • DeepStream 6.0.1 / 6.0 / 5.1 on Jetson platform

    CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo

Edit the config_infer_primary_yoloV6 file

Edit the config_infer_primary_yoloV6.txt file according to your model (example for YOLOv6-S 4.0 with 80 classes)

[property]
...
onnx-file=yolov6s.onnx
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYolo
...

NOTE: The YOLOv6 resizes the input with center padding. To get better accuracy, use

[property]
...
maintain-aspect-ratio=1
symmetric-padding=1
...

Edit the deepstream_app_config file

...
[primary-gie]
...
config-file=config_infer_primary_yoloV6.txt

Testing the model

deepstream-app -c deepstream_app_config.txt

NOTE: The TensorRT engine file may take a very long time to generate (sometimes more than 10 minutes).

NOTE: For more information about custom models configuration (batch-size, network-mode, etc), please check the docs/customModels.md file.