deepstream_yolo/docs/YOLOv5.md

YOLOv5 usage

NOTE: You can use the main branch of the YOLOv5 repo to convert all model versions.

NOTE: The yaml file is not required.

• Convert model
• Compile the lib
• Edit the config_infer_primary_yoloV5 file
• Edit the deepstream_app_config file
• Testing the model

Convert model

1. Download the YOLOv5 repo and install the requirements

git clone https://github.com/ultralytics/yolov5.git
cd yolov5
pip3 install -r requirements.txt

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the gen_wts_yoloV5.py file from DeepStream-Yolo/utils directory to the yolov5 folder.

3. Download the model

Download the pt file from YOLOv5 releases (example for YOLOv5s 6.1)

wget https://github.com/ultralytics/yolov5/releases/download/v6.1/yolov5s.pt

NOTE: You can use your custom model, but it is important to keep the YOLO model reference (yolov5_) in you cfg and weights/wts filenames to generate the engine correctly.

4. Convert model

Generate the cfg and wts files (example for YOLOv5s)

python3 gen_wts_yoloV5.py -w yolov5s.pt

NOTE: To convert a P6 model

--p6

NOTE: To change the inference size (defaut: 640)

-s SIZE
--size SIZE
-s HEIGHT WIDTH
--size HEIGHT WIDTH

Example for 1280

-s 1280

or

-s 1280 1280

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_yoloV5 file

Edit the config_infer_primary_yoloV5.txt file according to your model (example for YOLOv5s with 80 classes)

[property]
...
custom-network-config=yolov5s.cfg
model-file=yolov5s.wts
...
num-detected-classes=80
...

Edit the deepstream_app_config file

...
[primary-gie]
...
config-file=config_infer_primary_yoloV5.txt

Testing the model

deepstream-app -c deepstream_app_config.txt

NOTE: For more information about custom models configuration (batch-size, network-mode, etc), please check the docs/customModels.md file.