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# YOLOv8-TensorRT
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YOLOv8 using TensorRT accelerate !
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# Preprocessed ONNX model
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You can dowload the onnx model which is pretrained by https://github.com/ultralytics .
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[**YOLOv8-n**](https://triplemu.oss-cn-beijing.aliyuncs.com/YOLOv8/ONNX/yolov8n_nms.onnx?OSSAccessKeyId=LTAI5tN1dgmZD4PF8AJUXp3J&Expires=1772936700&Signature=r6HgJTTcCSAxQxD9bKO9qBTtigQ%3D)
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[**YOLOv8-s**](https://triplemu.oss-cn-beijing.aliyuncs.com/YOLOv8/ONNX/yolov8s_nms.onnx?OSSAccessKeyId=LTAI5tN1dgmZD4PF8AJUXp3J&Expires=1682936722&Signature=JjxQFx1YElcVdsCaMoj81KJ4a5s%3D)
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[**YOLOv8-m**](https://triplemu.oss-cn-beijing.aliyuncs.com/YOLOv8/ONNX/yolov8m_nms.onnx?OSSAccessKeyId=LTAI5tN1dgmZD4PF8AJUXp3J&Expires=1682936739&Signature=IRKBELdVFemD7diixxxgzMYqsWg%3D)
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[**YOLOv8-l**](https://triplemu.oss-cn-beijing.aliyuncs.com/YOLOv8/ONNX/yolov8l_nms.onnx?OSSAccessKeyId=LTAI5tN1dgmZD4PF8AJUXp3J&Expires=1682936763&Signature=RGkJ4G2XJ4J%2BNiki5cJi3oBkDnA%3D)
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[**YOLOv8-x**](https://triplemu.oss-cn-beijing.aliyuncs.com/YOLOv8/ONNX/yolov8x_nms.onnx?OSSAccessKeyId=LTAI5tN1dgmZD4PF8AJUXp3J&Expires=1673936778&Signature=3o%2F7QKhiZg1dW3I6sDrY4ug6MQU%3D)
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# Build TensorRT engine by ONNX
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## 1. By TensorRT Python api
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You can export TensorRT engine by [`build.py` ](build.py).
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Usage:
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``` shell
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python3 build.py --onnx yolov8s_nms.onnx --device cuda:0 --fp16
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```
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#### Description of all arguments
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- `--onnx` : The ONNX model you download.
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- `--device` : The CUDA deivce you export engine .
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- `--half` : Whether to export half-precision model.
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## 2. By trtexec tools
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You can export TensorRT engine by [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools.
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Usage:
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``` shell
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/usr/src/tensorrt/bin/trtexec --onnx=yolov8s_nms.onnx --saveEngine=yolov8s_nms.engine --fp16
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```
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***If you installed TensorRT by a debian package, then the installation path of `trtexec`
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is `/usr/src/tensorrt/bin/trtexec`***
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***If you installed TensorRT by a tar package, then the installation path of `trtexec` is under the `bin` folder in the
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path you decompressed***
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# Infer images by the engine which you export
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You can infer images with the engine by [`infer.py`](infer.py) .
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Usage:
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``` shell
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python3 infer.py --engine yolov8s_nms.engine --imgs data --show --out-dir outputs --device cuda:0
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```
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#### Description of all arguments
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- `--engine` : The Engine you export.
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- `--imgs` : The images path you want to detect.
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- `--show` : Whether to show detection results.
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- `--out-dir` : Where to save detection results images. It will not work when use `--show` flag.
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- `--device` : The CUDA deivce you use.
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- `--profile` : Profile the TensorRT engine.
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If you want to profile the TensorRT engine:
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Usage:
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``` shell
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python3 infer.py --engine yolov8s_nms.engine --profile
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```
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