deepstream_yolo/docs/YOLOv5.md

YOLOv5 usage

NOTE: You can use the master 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
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_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 7.0)

wget https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt

NOTE: You can use your custom model.

4. Convert model

Generate the ONNX model file (example for YOLOv5s)

python3 export_yoloV5.py -w yolov5s.pt --simplify --dynamic

NOTE: To simplify the ONNX model

--simplify

NOTE: To use dynamic batch-size

--dynamic

NOTE: To use implicit batch-size (example for batch-size = 4)

--batch 4

NOTE: If you are using DeepStream 5.1, use opset 12 or lower. The default opset is 17.

--opset 12

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 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 6.2 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 6.2 / 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_yoloV5 file

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

[property]
...
onnx-file=yolov5s.onnx
...
num-detected-classes=80
...
parse-bbox-func-name=NvDsInferParseYolo
...

NOTE: The YOLOv5 resizes the input with center padding. To get better accuracy, use

...
maintain-aspect-ratio=1
symmetric-padding=1
...

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

...
force-implicit-batch-dim=1
...

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: 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.