fendaq / yolov5-tensorrt

YOLOv5 in TensorRT

yolov5-tensorrt

port pytorch/onnx yolov5 model to run on a Jetson Nano

ipynb is for testing pytorch code and exporting onnx models using Google Colab

python code runs numpy/tensorrt implementation on Jetson Nano

├── python
│   ├── lib
|       ├── demo.py
|       ├── Processor.py
|       ├── Visualizer.py
|       ├── classes.py
|       └── models
|           ├── yolov5s-simple-32.trt
|           ├── yolov5s-simple-16.trt
|           └── yolov5s-simple.onnx
│   └── export_tensorrt.py

• convert yolov5 onnx model to tensorrt
• pre-process image
• run inference against input using tensorrt engine
• post process output (forward pass)
• apply nms thresholding on candidate boxes
• visualize results

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run demo

python3 demo.py -image=./path/to/image.jpg -model=./path/to/model.trt

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performance

for now, only testing initial inference performance
nms, and post processing are slow rn

model	fp precision	input size	time (ms)
small-simple	32	640x640x3	15.46
small-simple	16	640x640x3	9.47

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object probability