Update badges and ONNXRuntime usage (#184)

* Add pre-commit.ci badge

* Update emoji

* Fixing links

* Updating badge

* Update ONNXRuntime usage

README.md

@@ -6,10 +6,10 @@
 
 ______________________________________________________________________
 
-[Documentation](#%EF%B8%8F-usage) •
+[Documentation](#hammer_and_wrench-usage) •
 [Installation Instructions](#installation-and-inference-examples) •
-[Deployment](#-deployment) •
-[Contributing](#-contributing) •
+[Deployment](#rocket-deployment) •
+[Contributing](#thinking-contributing) •
 [Reporting Issues](https://github.com/zhiqwang/yolov5-rt-stack/issues/new?assignees=&labels=&template=bug-report.yml)
 
 ______________________________________________________________________
@@ -18,17 +18,18 @@ ______________________________________________________________________
 [![PyPI version](https://badge.fury.io/py/yolort.svg)](https://badge.fury.io/py/yolort)
 [![PyPI downloads](https://static.pepy.tech/personalized-badge/yolort?period=total&units=international_system&left_color=grey&right_color=blue&left_text=pypi%20downloads)](https://pepy.tech/project/yolort)
 [![Github downloads](https://img.shields.io/github/downloads/zhiqwang/yolov5-rt-stack/total?color=blue&label=downloads&logo=github&logoColor=lightgrey)](https://img.shields.io/github/downloads/zhiqwang/yolov5-rt-stack/total?color=blue&label=Downloads&logo=github&logoColor=lightgrey)
+[![license](https://img.shields.io/github/license/zhiqwang/yolov5-rt-stack?color=brightgreen)](LICENSE)
 
 [![CI testing](https://github.com/zhiqwang/yolov5-rt-stack/workflows/CI%20testing/badge.svg)](https://github.com/zhiqwang/yolov5-rt-stack/actions?query=workflow%3A%22CI+testing%22)
+[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/zhiqwang/yolov5-rt-stack/master.svg)](https://results.pre-commit.ci/latest/github/zhiqwang/yolov5-rt-stack/master)
 [![codecov](https://codecov.io/gh/zhiqwang/yolov5-rt-stack/branch/master/graph/badge.svg?token=1GX96EA72Y)](https://codecov.io/gh/zhiqwang/yolov5-rt-stack)
-[![license](https://img.shields.io/github/license/zhiqwang/yolov5-rt-stack?color=brightgreen)](LICENSE)
 [![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/yolort/shared_invite/zt-mqwc7235-940aAh8IaKYeWclrJx10SA)
 
 ______________________________________________________________________
 
 </div>
 
-## 🤗 Introduction
+## :hugs: Introduction
 
 **What it is.** Yet another implementation of Ultralytics's [YOLOv5](https://github.com/ultralytics/yolov5). `yolort` aims to make the training and inference of the object detection integrate more seamlessly together. `yolort` now adopts the same model structure as the official YOLOv5. The significant difference is that we adopt the dynamic shape mechanism, and within this, we can embed both pre-processing (`letterbox`) and post-processing (`nms`) into the model graph, which simplifies the deployment strategy. In this sense, `yolort` makes it possible to be deployed more friendly on `LibTorch`, `ONNXRuntime`, `TVM` and so on.
 
@@ -40,7 +41,7 @@ ______________________________________________________________________
 
 <a href=".github/zidane.jpg"><img src=".github/zidane.jpg" alt="YOLO inference demo" width="500"/></a>
 
-## 🆕 What's New
+## :new: What's New
 
 - *Sep. 24, 2021*. Add `ONNXRuntime` C++ interface example. Thanks to [itsnine](https://github.com/itsnine).
 - *Feb. 5, 2021*. Add `TVM` compile and inference notebooks.
@@ -50,7 +51,7 @@ ______________________________________________________________________
 - *Nov. 4, 2020*. Add `LibTorch` C++ inference example.
 - *Oct. 8, 2020*. Support exporting to `TorchScript` model.
 
-## 🛠️ Usage
+## :hammer_and_wrench: Usage
 
 There are no extra compiled components in `yolort` and package dependencies are minimal, so the code is very simple to use.
 
@@ -82,7 +83,7 @@ There are no extra compiled components in `yolort` and package dependencies are
   pip install -U 'git+https://github.com/ppwwyyxx/cocoapi.git#subdirectory=PythonAPI'
   ```
 
-- To read a source of image(s) and detect its objects 🔥
+- To read a source of image(s) and detect its objects :fire:
 
   ```python
   from yolort.models import yolov5s
@@ -121,27 +122,27 @@ img_path = "test/assets/bus.jpg"
 predictions = model.predict(img_path)
 ```
 
-## 🚀 Deployment
+## :rocket: Deployment
 
 ### Inference on `LibTorch` backend
 
-We provide a [notebook](notebooks/inference-pytorch-export-libtorch.ipynb) to demonstrate how the model is transformed into `torchscript`. And we provide an [C++ example](./deployment/libtorch) of how to infer with the transformed `torchscript` model. For details see the [GitHub Actions](.github/workflows/ci_test.yml).
+We provide a [notebook](notebooks/inference-pytorch-export-libtorch.ipynb) to demonstrate how the model is transformed into `torchscript`. And we provide an [C++ example](deployment/libtorch) of how to infer with the transformed `torchscript` model. For details see the [GitHub Actions](.github/workflows/ci_test.yml).
 
 ### Inference on `ONNXRuntime` backend
 
-TBD
+On the `ONNXRuntime` front you can use the [C++ example](deployment/onnxruntime), and we also provide a tutorial [export-onnx-inference-onnxruntime](notebooks/export-onnx-inference-onnxruntime.ipynb) for using the `ONNXRuntime`.
 
-## 🎨 Model Graph Visualization
+## :art: Model Graph Visualization
 
 Now, `yolort` can draw the model graph directly, checkout our [model-graph-visualization](notebooks/model-graph-visualization.ipynb) notebook to see how to use and visualize the model graph.
 
 <a href="notebooks/assets/yolov5.detail.svg"><img src="notebooks/assets/yolov5.detail.svg" alt="YOLO model visualize" width="500"/></a>
 
-## 🎓 Acknowledgement
+## :mortar_board: Acknowledgement
 
 - The implementation of `yolov5` borrow the code from [ultralytics](https://github.com/ultralytics/yolov5).
 - This repo borrows the architecture design and part of the code from [torchvision](https://github.com/pytorch/vision).
 
-## 🤗 Contributing
+## :thinking: Contributing
 
 We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. *BTW, leave a 🌟 if you liked it, and this is the easiest way to support us* :)