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FastDeploy/examples/vision/detection/yolov7/quantize/cpp
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..
CMakeLists.txt
Add Examples to deploy quantized models (#342)
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infer.cc
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README_CN.md
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README.md
[Doc] Update English version of some documents (#1084)
2023-01-06 18:01:34 +08:00

README.md

English | 简体中文

YOLOv7 Quantification Model C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of YOLOv7 quantification models on CPU/GPU.

Prepare the deployment

FastDeploy Environment Preparation

Prepare the quantification model

    1. Users can directly deploy quantized models provided by FastDeploy.
    1. Or users can use the One-click auto-compression tool provided by FastDeploy to automatically conduct quantification model for deployment.

Example: quantized YOLOv7 model

The compilation and deployment 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.

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 yolov7 quantification model files and test images provided by FastDeploy
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov7_quant.tar
tar -xvf yolov7_quant.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg


# Use ONNX Runtime quantification model on CPU
./infer_demo yolov7_quant 000000014439.jpg 0
# Use TensorRT quantification model on GPU
./infer_demo yolov7_quant 000000014439.jpg 1
# Use Paddle-TensorRT quantification model on GPU
./infer_demo yolov7_quant 000000014439.jpg 2