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YOLOv7.md

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YOLOv7 usage

NOTE: The yaml file is not required.

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 hhttps://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. Convert model

Generate the cfg and wts files (example for YOLOv7)

python3 gen_wts_yoloV7.py -w yolov7.pt

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 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_yoloV7 file

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

[property]
...
custom-network-config=yolov7.cfg
model-file=yolov7.wts
...

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