deepstream_yolo/docs/YOLOv7.md

YOLOv7 usage

NOTE: The yaml file is not required.

• Convert model
• Compile the lib
• Edit the config_infer_primary_yoloV7 file
• Edit the deepstream_app_config file
• Testing the model

Convert model

1. Download the YOLOv7 repo and install the requirements

git clone https://github.com/WongKinYiu/yolov7.git
cd yolov7
pip3 install -r requirements.txt

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the gen_wts_yoloV7.py file from DeepStream-Yolo/utils directory to the yolov7 folder.

3. Download the model

Download the pt file from YOLOv7 releases (example for YOLOv7)

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt

NOTE: You can use your custom model, but it is important to keep the YOLO model reference (yolov7_) in you cfg and weights/wts filenames to generate the engine correctly.

4. Reparameterize your model

YOLOv7 and it's variants can't be directly converted to engine file. Therefore, you will have to reparameterize your model using the code here. Make sure to convert your checkpoints in yolov7 repository, and then save your reparmeterized checkpoints for conversion in the next step.

5. Convert model

Generate the cfg and wts files (example for YOLOv7)

python3 gen_wts_yoloV7.py -w yolov7.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

6. 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 6.2 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 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 on Jetson platform

  CUDA_VER=10.2 make -C nvdsinfer_custom_impl_Yolo
  

Edit the config_infer_primary_yoloV7 file

Edit the config_infer_primary_yoloV7.txt file according to your model (example for YOLOv7 with 80 classes)

[property]
...
custom-network-config=yolov7.cfg
model-file=yolov7.wts
...
num-detected-classes=80
...

Edit the deepstream_app_config file

...
[primary-gie]
...
config-file=config_infer_primary_yoloV7.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.