deepstream_yolo/docs/YOLOX.md

YOLOX usage

NOTE: The yaml file is not required.

• Convert model
• Compile the lib
• Edit the config_infer_primary_yolox file
• Edit the deepstream_app_config file
• Testing the model

Convert model

1. Download the YOLOX repo and install the requirements

git clone https://github.com/Megvii-BaseDetection/YOLOX
cd YOLOX
pip3 install -r requirements.txt

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the gen_wts_yolox.py file from DeepStream-Yolo/utils directory to the YOLOX folder.

3. Download the model

Download the pth file from YOLOX releases (example for YOLOX-s standard)

wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.pth

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

4. Convert model

Generate the cfg and wts files (example for YOLOX-s standard)

python3 gen_wts_yolox.py -w yolox_s.pth -e exps/default/yolox_s.py

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 6.1.1 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 6.1.1 / 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_yolox file

Edit the config_infer_primary_yolox.txt file according to your model (example for YOLOX-s standard)

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

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

NOTE: The YOLOX standard uses no normalization on the image preprocess. It is important to change the net-scale-factor according to the trained values.

net-scale-factor=0

NOTE: The YOLOX 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_yolox.txt

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

Testing the model

deepstream-app -c deepstream_app_config.txt