YOLOR usage

NOTE: You need to use the main branch of the YOLOR repo to convert the model.

NOTE: The cfg is required.

Convert model
Compile the lib
Edit the config_infer_primary_yolor file
Edit the deepstream_app_config file
Testing the model

Convert model

1. Download the YOLOR repo and install the requirements

git clone https://github.com/WongKinYiu/yolor.git
cd yolor
pip3 install -r requirements.txt

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the gen_wts_yolor.py file from DeepStream-Yolo/utils directory to the yolor folder.

3. Download the model

Download the pt file from YOLOR repo.

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

4. Convert model

Generate the cfg and wts files (example for YOLOR-CSP)

python3 gen_wts_yolor.py -w yolor_csp.pt -c cfg/yolor_csp.cfg

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_yolor file

Edit the config_infer_primary_yolor.txt file according to your model (example for YOLOR-CSP)

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

Edit the deepstream_app_config.txt file

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

Testing the model

deepstream-app -c deepstream_app_config.txt

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