Update README.md, deploy model with tensorrt

[wang-xinyu]

🛠️ PR Summary

_{Made with ❤️ by Ultralytics Actions}

🌟 Summary

Added TensorRT Deployment reference to the README.

📊 Key Changes

• Included a new link in the README to a TensorRT Deployment resource.

🎯 Purpose & Impact

• 🚀 Purpose: Provides users with a reference to TensorRT Deployment, likely to assist with more efficient model deployment on NVIDIA GPUs.
• 💡 Impact: Users interested in optimizing their YOLOv5 models for production may benefit from easier access to relevant information on deploying with TensorRT, potentially leading to enhanced performance and faster inference times.

[glenn-jocher]

Great, thanks! I've updated the wiki and the export tutorial also at https://docs.ultralytics.com/yolov5/tutorials/model_export

[wang-xinyu]

@glenn-jocher You are welcome. Actually we were not using ONNX, we define the yolov5-s/m/l/x network directly with TensorRT API.

We convert the .pt weight file to .wts(plain text), and then load .wts into tensorrt and build tensorrt model.

this is my repo intro

TensorRTx

TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs. As we know, tensorrt has builtin parsers, including caffeparser, uffparser, onnxparser, etc. But when we use these parsers, we often run into some "unsupported operations or layers" problems, especially some state-of-the-art models are using new type of layers.

So why don't we just skip all parsers? We just use TensorRT network definition APIs to build the whole network, it's not so complicated.

I wrote this project to get familiar with tensorrt API, and also to share and learn from the community.

All the models are implemented in pytorch or mxnet first, and export a weights file xxx.wts, and then use tensorrt to load weights, define network and do inference. Some pytorch implementations can be found in my repo Pytorchx, the remaining are from polular open-source implementations.

[glenn-jocher]

@wang-xinyu oh wow ok! Did not know that, I will correct the export tutorial.