YOLOv7_Pose.md

YOLOv7-Pose usage

NOTE: The yaml file is not required.

• Convert model
• Edit the config_infer_primary_yoloV7_pose file

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
pip3 install onnx onnxsim onnxruntime

NOTE: It is recommended to use Python virtualenv.

2. Copy conversor

Copy the export_yoloV7_pose.py file from DeepStream-Yolo-Pose/utils directory to the yolov7 folder.

3. Download the model

Download the pt file from YOLOv7 releases (example for YOLOv7-w6-Pose)

wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt

NOTE: You can use your custom model.

4. Reparameterize your model (for custom models)

Custom YOLOv7 models cannot be directly converted to engine file. Therefore, you will have to reparameterize your model using the code here. Make sure to convert your custom checkpoints in YOLOv7 repository, and then save your reparmeterized checkpoints for conversion in the next step.

5. Convert model

Generate the ONNX model file (example for YOLOv7-w6-Pose)

python3 export_yoloV7_pose.py -w yolov7-w6-pose.pt --dynamic --p6

NOTE: To convert a P6 model

--p6

NOTE: To change the inference size (defaut: 640 / 1280 for --p6 models)

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

Example for 1280

-s 1280

or

-s 1280 1280

NOTE: To simplify the ONNX model (DeepStream >= 6.0)

--simplify

NOTE: To use dynamic batch-size (DeepStream >= 6.1)

--dynamic

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

--batch 4

6. Copy generated files

Copy the generated ONNX model file to the DeepStream-Yolo-Pose folder.

Edit the config_infer_primary_yoloV7_pose file

Edit the config_infer_primary_yoloV7_pose.txt file according to your model (example for YOLOv7-w6-Pose)

[property]
...
onnx-file=yolov7-w6-pose.onnx
...
parse-bbox-func-name=NvDsInferParseYoloPose
...