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1. using yolov5 export file, python3 export.py --weights yolov5m_shear.pt --include onnx --data coco128.yaml --device 0 --batch-size 4
2. Convert batch parameter to 'b' using models/dynmic.py.
3. use following trtexec command for conversion (change the input resolution and batch size accordigly)
   trtexec --onnx=dynamic.onnx --fp16 --minShapes=images:1x3x640x640 --optShapes=images:4x3x640x640 --maxShapes=images:8x3x640x640 --useCudaGraph --saveEngine= < output name >_dynamic.engine

1. using yolov5 export file,python3 export.py --weights yolov5m_shear.pt --include onnx --data coco128.yaml --device 0
2. trtexec --onnx=yolov5m.onnx --fp16 --saveEngine=yolov5m_static.engine --useCudaGraph

For batch inference,

1. Alaways use a dynamic engine file.
2. In config.py
   • Make the TensorRT.batch True
   • Set the correct batch size of the converted engine file in TensorRT.batch_size
3. Make shure the camera fps is set to a divisible value of the batch_size.