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https://github.com/triple-Mu/YOLOv8-TensorRT

YOLOv8 using TensorRT accelerate !
https://github.com/triple-Mu/YOLOv8-TensorRT

deepstream detection jetson onnx pose segment tensorrt yolov8

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YOLOv8 using TensorRT accelerate !

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README 

        
# YOLOv8-TensorRT



`YOLOv8` using TensorRT accelerate !



---

[![Build Status](https://img.shields.io/endpoint.svg?url=https%3A%2F%2Factions-badge.atrox.dev%2Fatrox%2Fsync-dotenv%2Fbadge&style=flat)](https://github.com/triple-Mu/YOLOv8-TensorRT)

[![Python Version](https://img.shields.io/badge/Python-3.8--3.10-FFD43B?logo=python)](https://github.com/triple-Mu/YOLOv8-TensorRT)

[![img](https://badgen.net/badge/icon/tensorrt?icon=azurepipelines&label)](https://developer.nvidia.com/tensorrt)

[![C++](https://img.shields.io/badge/CPP-11%2F14-yellow)](https://github.com/triple-Mu/YOLOv8-TensorRT)

[![img](https://badgen.net/github/license/triple-Mu/YOLOv8-TensorRT)](https://github.com/triple-Mu/YOLOv8-TensorRT/blob/main/LICENSE)

[![img](https://badgen.net/github/prs/triple-Mu/YOLOv8-TensorRT)](https://github.com/triple-Mu/YOLOv8-TensorRT/pulls)

[![img](https://img.shields.io/github/stars/triple-Mu/YOLOv8-TensorRT?color=ccf)](https://github.com/triple-Mu/YOLOv8-TensorRT)



---



# Prepare the environment



1. Install `CUDA` follow [`CUDA official website`](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#download-the-nvidia-cuda-toolkit).



   🚀 RECOMMENDED `CUDA` >= 11.4



2. Install `TensorRT` follow [`TensorRT official website`](https://developer.nvidia.com/nvidia-tensorrt-8x-download).



   🚀 RECOMMENDED `TensorRT` >= 8.4



2. Install python requirements.



   ``` shell

   pip install -r requirements.txt

   ```



3. Install [`ultralytics`](https://github.com/ultralytics/ultralytics) package for ONNX export or TensorRT API building.



   ``` shell

   pip install ultralytics

   ```



5. Prepare your own PyTorch weight such as `yolov8s.pt` or `yolov8s-seg.pt`.



***NOTICE:***



Please use the latest `CUDA` and `TensorRT`, so that you can achieve the fastest speed !



If you have to use a lower version of `CUDA` and `TensorRT`, please read the relevant issues carefully !



# Normal Usage



If you get ONNX from origin [`ultralytics`](https://github.com/ultralytics/ultralytics) repo, you should build engine by yourself.



You can only use the `c++` inference code to deserialize the engine and do inference.



You can get more information in [`Normal.md`](docs/Normal.md) !



Besides, other scripts won't work.



# Export End2End ONNX with NMS



You can export your onnx model by `ultralytics` API and add postprocess such as bbox decoder and `NMS` into ONNX model at the same time.



``` shell

python3 export-det.py \

--weights yolov8s.pt \

--iou-thres 0.65 \

--conf-thres 0.25 \

--topk 100 \

--opset 11 \

--sim \

--input-shape 1 3 640 640 \

--device cuda:0

```



#### Description of all arguments



- `--weights` : The PyTorch model you trained.

- `--iou-thres` : IOU threshold for NMS plugin.

- `--conf-thres` : Confidence threshold for NMS plugin.

- `--topk` : Max number of detection bboxes.

- `--opset` : ONNX opset version, default is 11.

- `--sim` : Whether to simplify your onnx model.

- `--input-shape` : Input shape for you model, should be 4 dimensions.

- `--device` : The CUDA deivce you export engine .



You will get an onnx model whose prefix is the same as input weights.



###  Just Taste First



If you just want to taste first, you can download the onnx model which are exported by `YOLOv8` package and modified by me.



[**YOLOv8-n**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8n.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=2690974569&Signature=3ct9pnRygBduWdgAtfKOQAt4PeU%3D)



[**YOLOv8-s**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8s.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=10000000001690974000&Signature=cbHjUwmRsYdvilcirzjBI6%2BzmvI%3D)



[**YOLOv8-m**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8m.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=101690974603&Signature=XnJnQqbKsnJSKSgqVQ41kxoeETU%3D)



[**YOLOv8-l**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8l.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=2690974619&Signature=djxvNzcaFosHrMS5ylWh1R0%2Ff8E%3D)



[**YOLOv8-x**](https://triplemu-shared.oss-cn-beijing.aliyuncs.com/models/yolov8x.onnx?OSSAccessKeyId=LTAI5tNk9iiMqhFC64jCcgpv&Expires=2690974637&Signature=DMmuT2wlfBzai%2BBpYJFcmNbkMKU%3D)



# Build End2End Engine from ONNX

### 1. Build Engine by TensorRT ONNX Python api



You can export TensorRT engine from ONNX by [`build.py` ](build.py).



Usage:



``` shell

python3 build.py \

--weights yolov8s.onnx \

--iou-thres 0.65 \

--conf-thres 0.25 \

--topk 100 \

--fp16  \

--device cuda:0

```



#### Description of all arguments



- `--weights` : The ONNX model you download.

- `--iou-thres` : IOU threshold for NMS plugin.

- `--conf-thres` : Confidence threshold for NMS plugin.

- `--topk` : Max number of detection bboxes.

- `--fp16` : Whether to export half-precision engine.

- `--device` : The CUDA deivce you export engine .



You can modify `iou-thres` `conf-thres` `topk` by yourself.



### 2. Export Engine by Trtexec Tools



You can export TensorRT engine by [`trtexec`](https://github.com/NVIDIA/TensorRT/tree/main/samples/trtexec) tools.



Usage:



``` shell

/usr/src/tensorrt/bin/trtexec \

--onnx=yolov8s.onnx \

--saveEngine=yolov8s.engine \

--fp16

```



**If you installed TensorRT by a debian package, then the installation path of `trtexec`

is `/usr/src/tensorrt/bin/trtexec`**



**If you installed TensorRT by a tar package, then the installation path of `trtexec` is under the `bin` folder in the path you decompressed**



# Build TensorRT Engine by TensorRT API



Please see more information in [`API-Build.md`](docs/API-Build.md)



***Notice !!!*** We don't support YOLOv8-seg model now !!!



# Inference



## 1. Infer with python script



You can infer images with the engine by [`infer-det.py`](infer-det.py) .



Usage:



``` shell

python3 infer-det.py \

--engine yolov8s.engine \

--imgs data \

--show \

--out-dir outputs \

--device cuda:0

```



#### Description of all arguments



- `--engine` : The Engine you export.

- `--imgs` : The images path you want to detect.

- `--show` : Whether to show detection results.

- `--out-dir` : Where to save detection results images. It will not work when use `--show` flag.

- `--device` : The CUDA deivce you use.

- `--profile` : Profile the TensorRT engine.



## 2. Infer with C++



You can infer with c++ in [`csrc/detect/end2end`](csrc/detect/end2end) .



### Build:



Please set you own librarys in [`CMakeLists.txt`](csrc/detect/end2end/CMakeLists.txt) and modify `CLASS_NAMES` and `COLORS` in [`main.cpp`](csrc/detect/end2end/main.cpp).



``` shell

export root=${PWD}

cd csrc/detect/end2end

mkdir -p build && cd build

cmake ..

make

mv yolov8 ${root}

cd ${root}

```



Usage:



``` shell

# infer image

./yolov8 yolov8s.engine data/bus.jpg

# infer images

./yolov8 yolov8s.engine data

# infer video

./yolov8 yolov8s.engine data/test.mp4 # the video path

```



# TensorRT Segment Deploy



Please see more information in [`Segment.md`](docs/Segment.md)



# TensorRT Pose Deploy



Please see more information in [`Pose.md`](docs/Pose.md)



# TensorRT Cls Deploy



Please see more information in [`Cls.md`](docs/Cls.md)



# DeepStream Detection Deploy



See more in [`README.md`](csrc/deepstream/README.md)



# Jetson Deploy



Only test on `Jetson-NX 4GB`.

See more in [`Jetson.md`](docs/Jetson.md)



# Profile you engine



If you want to profile the TensorRT engine:



Usage:



``` shell

python3 trt-profile.py --engine yolov8s.engine --device cuda:0

```



# Refuse To Use PyTorch for Model Inference !!!



If you need to break away from pytorch and use tensorrt inference,

you can get more information in [`infer-det-without-torch.py`](infer-det-without-torch.py),

the usage is the same as the pytorch version, but its performance is much worse.



You can use `cuda-python` or `pycuda` for inference.

Please install by such command:



```shell

pip install cuda-python

# or

pip install pycuda

```



Usage:



``` shell

python3 infer-det-without-torch.py \

--engine yolov8s.engine \

--imgs data \

--show \

--out-dir outputs \

--method cudart

```



#### Description of all arguments



- `--engine` : The Engine you export.

- `--imgs` : The images path you want to detect.

- `--show` : Whether to show detection results.

- `--out-dir` : Where to save detection results images. It will not work when use `--show` flag.

- `--method` : Choose `cudart` or `pycuda`, default is `cudart`.