deepstream_yolo/docs/PPYOLOE.md

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
• Compile the lib
• Edit the config_infer_primary_ppyoloe_plus file
• Edit the deepstream_app_config file
• Testing the model

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 --dynamic

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

--simplify

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

--dynamic

NOTE: To use implicit 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 11.

--opset 12

5. Copy generated files

Copy the generated ONNX model file and labels.txt file (if generated) 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_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
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYoloE
...

NOTE: If you are using 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

[property]
...
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.

[property]
...
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

[property]
...
net-scale-factor=0.0173520735727919486
offsets=123.675;116.28;103.53
...

NOTE: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line

[property]
...
force-implicit-batch-dim=1
...

Edit the deepstream_app_config file

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

NOTE: If you are using 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.