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PPYOLOE.md

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PP-YOLOE / PP-YOLOE+ usage

NOTE: You can use the release/2.6 branch of the PPYOLOE repo to convert all model versions.

Convert model

1. Download the PaddleDetection repo and install the requirements

https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.6/docs/tutorials/INSTALL.md

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_ppyoloe.py file from DeepStream-Yolo/utils directory to the PaddleDetection folder.

3. Download the model

Download the pdparams file from PP-YOLOE releases (example for PP-YOLOE+_s)

wget https://paddledet.bj.bcebos.com/models/ppyoloe_plus_crn_s_80e_coco.pdparams

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for PP-YOLOE+_s)

pip3 install onnx onnxsim onnxruntime
python3 export_ppyoloe.py -w ppyoloe_plus_crn_s_80e_coco.pdparams -c configs/ppyoloe/ppyoloe_plus_crn_s_80e_coco.yml --simplify

5. Copy generated files

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_ppyoloe_plus file

Edit the config_infer_primary_ppyoloe_plus.txt file according to your model (example for PP-YOLOE+_s with 80 classes)

[property]
...
onnx-file=ppyoloe_plus_crn_s_80e_coco.onnx
model-engine-file=ppyoloe_plus_crn_s_80e_coco.onnx_b1_gpu0_fp32.engine
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYoloE
...

NOTE: If you use the legacy model, you should edit the config_infer_primary_ppyoloe.txt file.

NOTE: The PP-YOLOE+ and PP-YOLOE legacy do not resize the input with padding. To get better accuracy, use

maintain-aspect-ratio=0

NOTE: The PP-YOLOE+ uses zero mean normalization on the image preprocess. It is important to change the net-scale-factor according to the trained values.

net-scale-factor=0.0039215697906911373

NOTE: The PP-YOLOE legacy uses normalization on the image preprocess. It is important to change the net-scale-factor and offsets according to the trained values.

Default: mean = 0.485, 0.456, 0.406 and std = 0.229, 0.224, 0.225

net-scale-factor=0.0173520735727919486
offsets=123.675;116.28;103.53

Edit the deepstream_app_config file

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

NOTE: If you use the legacy model, you should edit it to config_infer_primary_ppyoloe.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.