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English | [简体中文](README_CN.md)
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# YOLOv5s量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv5量化模型在CPU/GPU上的部署推理加速.
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`infer.py` in this directory can help you quickly complete the inference acceleration of YOLOv5s quantization model deployment on CPU/GPU.
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## 部署准备
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### FastDeploy环境准备
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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## Deployment Preparations
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### FastDeploy Environment Preparations
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- 1. For the software and hardware requirements, please refer to [FastDeploy Environment Requirements](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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- 2. For the installation of FastDeploy Python whl package, please refer to [FastDeploy Python Installation](../../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
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### Quantized Model Preparations
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- 1. You can directly use the quantized model provided by FastDeploy for deployment..
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- 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.
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## 以量化后的YOLOv5s模型为例, 进行部署
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## Take the Quantized YOLOv5s Model as an example for Deployment
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```bash
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#下载部署示例代码
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# Download sample deployment code.
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolov5/quantize/python
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#下载FastDeloy提供的yolov5s量化模型文件和测试图片
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# Download the yolov5s quantized model and test images provided by FastDeloy.
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
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tar -xvf yolov5s_quant.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用ONNX Runtime推理量化模型
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# Use ONNX Runtime inference quantization model on CPU.
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend ort
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# 在GPU上使用TensorRT推理量化模型
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# Use TensorRT inference quantization model on GPU.
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt
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# 在GPU上使用Paddle-TensorRT推理量化模型
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# Use Paddle-TensorRT inference quantization model on GPU.
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend pptrt
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```
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@@ -0,0 +1,32 @@
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[English](README.md) | 简体中文
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# YOLOv5s量化模型 Python部署示例
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本目录下提供的`infer.py`,可以帮助用户快速完成YOLOv5量化模型在CPU/GPU上的部署推理加速.
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## 部署准备
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### FastDeploy环境准备
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### 量化模型准备
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- 1. 用户可以直接使用由FastDeploy提供的量化模型进行部署.
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- 2. 用户可以使用FastDeploy提供的[一键模型自动化压缩工具](../../../../../../tools/common_tools/auto_compression/),自行进行模型量化, 并使用产出的量化模型进行部署.
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## 以量化后的YOLOv5s模型为例, 进行部署
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```bash
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#下载部署示例代码
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/vision/detection/yolov5/quantize/python
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#下载FastDeloy提供的yolov5s量化模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_quant.tar
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tar -xvf yolov5s_quant.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# 在CPU上使用ONNX Runtime推理量化模型
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device cpu --backend ort
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# 在GPU上使用TensorRT推理量化模型
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend trt
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# 在GPU上使用Paddle-TensorRT推理量化模型
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python infer.py --model yolov5s_quant --image 000000014439.jpg --device gpu --backend pptrt
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```
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