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infer.py

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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

YOLOv6 Quantification Model Python Deployment Example

This directory provides examples that infer.py fast finishes the deployment of YOLOv6 quantification models on CPU/GPU.

Prepare the deployment

FastDeploy Environment Preparation

1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
1. Install FastDeploy Python whl package. Refer to FastDeploy Python Installation

Prepare the quantification model

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

Example: quantized YOLOv6 model

# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd examples/slim/yolov6/python

# Download yolov6 quantification model files and test images provided by FastDeploy
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_qat_model_new.tar
tar -xvf yolov6s_qat_model.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# Use ONNX Runtime quantification model on CPU
python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device cpu --backend ort
# Use TensorRT quantification model on GPU
python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend trt
# Use Paddle-TensorRT quantification model on GPU
python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend pptrt