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YOLONAS.md

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YOLO-NAS usage

NOTE: The yaml file is not required.

Convert model

1. Download the YOLO-NAS repo and install the requirements

git clone https://github.com/Deci-AI/super-gradients.git
cd super-gradients
pip3 install -r requirements.txt
python3 setup.py install
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_yolonas.py file from DeepStream-Yolo/utils directory to the super-gradients folder.

3. Download the model

Download the pth file from YOLO-NAS releases (example for YOLO-NAS S)

wget https://sghub.deci.ai/models/yolo_nas_s_coco.pth

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for YOLO-NAS S)

python3 export_yolonas.py -m yolo_nas_s -w yolo_nas_s_coco.pth --simplify --dynamic

NOTE: Model names

-m yolo_nas_s

or

-m yolo_nas_m

or

-m yolo_nas_l

NOTE: To change the inference size (defaut: 640)

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

Example for 1280

-s 1280

or

-s 1280 1280

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 6.2 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 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 on Jetson platform

    CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo

Edit the config_infer_primary_yolonas file

Edit the config_infer_primary_yolonas.txt file according to your model (example for YOLO-NAS S with 80 classes)

[property]
...
onnx-file=yolo_nas_s_coco.onnx
model-engine-file=yolo_nas_s_coco.onnx_b1_gpu0_fp32.engine
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYoloE
...

NOTE: The YOLO-NAS resizes the input with left/top padding. To get better accuracy, use

maintain-aspect-ratio=1
symmetric-padding=0

Edit the deepstream_app_config file

...
[primary-gie]
...
config-file=config_infer_primary_yolonas.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.