English | [简体中文](README_CN.md)
# ResNet C++ Deployment Example

This directory provides examples that `infer.cc` fast finishes the deployment of ResNet models on CPU/GPU and GPU accelerated by TensorRT. 

Before deployment, two steps require confirmation.

- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).  
- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).

Taking ResNet50 inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0)  is required to support this model.

```bash
mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# Download the ResNet50 model file and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg


# CPU inference
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 0
# GPU inference
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 1
# TensorRT Inference on GPU
./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 2
```

The above command works for Linux or MacOS. Refer to: 
- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md)  for SDK use-pattern in Windows

## ResNet C++ Interface 

### ResNet Class 

```c++

fastdeploy::vision::classification::ResNet(
        const std::string& model_file,
        const std::string& params_file = "",
        const RuntimeOption& custom_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)
```


**Parameter**

> * **model_file**(str): Model file path 
> * **params_file**(str): Parameter file path 
> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
> * **model_format**(ModelFormat): Model format. ONNX format by default

#### Predict Function

> ```c++
> ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1)
> ```
>
> Model prediction interface. Input images and output results directly.
>
> **Parameter**
>
> > * **im**: Input images in HWC or BGR format
> > * **result**: The classification result, including label_id, and the corresponding confidence. Refer to [Visual Model Prediction Results](../../../../../docs/api/vision_results/)  for the description of ClassifyResult
> > * **topk**(int): Return the topk classification results with the highest prediction probability. Default 1


- [Model Description](../../)
- [Python Deployment](../python)
- [Vision Model prediction results](../../../../../docs/api/vision_results/)
- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)