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# PaddleClas Quantitative Model C++ Deployment Example
 `infer.cc` in this directory can help you quickly complete the inference acceleration of PaddleClas quantization model deployment on CPU/GPU.

## Deployment Preparations
### FastDeploy Environment Preparations
- 1. For the software and hardware requirements, please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
- 2. For the installation of FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).

### Quantized Model Preparations
- 1. You can directly use the quantized model provided by FastDeploy for deployment.
- 2. You can use [one-click automatical compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to quantize model by yourself, and use the generated quantized model for deployment.(Note: The quantized classification model still needs the inference_cls.yaml file in the FP32 model folder. Self-quantized model folder does not contain this yaml file, you can copy it from the FP32 model folder to the quantized model folder.)

## Take the Quantized PP-YOLOE-l Model as an example for Deployment, FastDeploy version 0.7.0 or higher is required (x.x.x>=0.7.0)
Run the following commands in this directory to compile and deploy the quantized model.
```bash
mkdir build
cd build
# Download pre-compiled FastDeploy libraries. You can choose the appropriate version from `pre-compiled FastDeploy libraries` 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_Vd quantized model and test images provided by FastDeloy. 
wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar
tar -xvf resnet50_vd_ptq.tar
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg


# Use ONNX Runtime inference quantization model on CPU.
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 0
# Use TensorRT inference quantization model on GPU.
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 1
# Use Paddle-TensorRT inference quantization model on GPU.
./infer_demo resnet50_vd_ptq ILSVRC2012_val_00000010.jpeg 2
```