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32 lines
1.8 KiB
Markdown
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32 lines
1.8 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# YOLOv6 Quantification Model Python Deployment Example
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This directory provides examples that `infer.py` fast finishes the deployment of YOLOv6 quantification models on CPU/GPU.
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## Prepare the deployment
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### FastDeploy Environment Preparation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install FastDeploy Python whl package. Refer to [FastDeploy Python Installation](../../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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### Prepare the quantification model
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- 1. Users can directly deploy quantized models provided by FastDeploy.
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- 2. ii. Or users can use the [One-click auto-compression tool](../../../../../../tools/common_tools/auto_compression/) provided by FastDeploy to automatically conduct quantification model for deployment.
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## Example: quantized YOLOv6 model
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```bash
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# Download the example code for deployment
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git clone https://github.com/PaddlePaddle/FastDeploy.git
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cd examples/slim/yolov6/python
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# Download yolov6 quantification model files and test images provided by FastDeploy
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s_qat_model_new.tar
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tar -xvf yolov6s_qat_model.tar
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wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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# Use ONNX Runtime quantification model on CPU
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device cpu --backend ort
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# Use TensorRT quantification model on GPU
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend trt
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# Use Paddle-TensorRT quantification model on GPU
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python infer.py --model yolov6s_qat_model --image 000000014439.jpg --device gpu --backend pptrt
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```
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