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PP-YOLOE usage

Convert model

1. Download the PaddleDetection repo and install the requirements

https://github.com/PaddlePaddle/PaddleDetection/blob/release/2.4/docs/tutorials/INSTALL.md

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the gen_wts_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_crn_s_400e_coco.pdparams

NOTE: You can use your custom model, but it is important to keep the YOLO model reference (ppyoloe_) in you cfg and weights/wts filenames to generate the engine correctly.

4. Convert model

Generate the cfg and wts files (example for PP-YOLOE-s)

python3 gen_wts_ppyoloe.py -w ppyoloe_crn_s_400e_coco.pdparams -c configs/ppyoloe/ppyoloe_crn_s_400e_coco.yml

5. Copy generated files

Copy the generated cfg and wts files to the DeepStream-Yolo folder.

Compile the lib

Open the DeepStream-Yolo folder and compile the lib

  • 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 6.1 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 file

Edit the config_infer_primary_ppyoloe.txt file according to your model (example for PP-YOLOE-s)

[property]
...
custom-network-config=ppyoloe_crn_s_400e_coco.cfg
model-file=ppyoloe_crn_s_400e_coco.wts
...

NOTE: The PP-YOLOE 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.txt

Testing the model

deepstream-app -c deepstream_app_config.txt