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cpp

[Doc] Update English version of some documents (#1084 )

2023-01-06 18:01:34 +08:00

python

[Doc] Update English version of some documents (#1084 )

2023-01-06 18:01:34 +08:00

README_CN.md

[Doc]Add English version of documents in examples (#1070 )

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README.md

[Doc]Add English version of documents in examples (#1070 )

2023-01-06 09:34:28 +08:00

README.md

English | 简体中文

YOLOv7 Quantized Model Deployment

FastDeploy supports the deployment of quantized models and provides a one-click model quantization tool. Users can use the one-click model quantization tool to quantize and deploy the models themselves or download the quantized models provided by FastDeploy directly for deployment.

FastDeploy One-Click Model Quantization Tool

FastDeploy provides a one-click quantization tool that allows users to quantize a model simply with a configuration file. For detailed tutorial, please refer to : One-Click Model Quantization Tool

Download Quantized YOLOv7 Model

Users can also directly download the quantized models in the table below for deployment.

Model	Inference Backend	Hardware	FP32 Inference Time Delay	FP32 Inference Time Delay	Acceleration ratio	FP32 mAP	INT8 mAP	Method
YOLOv7	TensorRT	GPU	24.57	9.40	2.61	51.1	50.8	Quantized distillation training
YOLOv7	Paddle Inference	CPU	1022.55	490.87	2.08	51.1	46.3	Quantized distillation training

The data in the above table shows the end-to-end inference performance of FastDeploy deployment before and after model quantization.

The test images are from COCO val2017.
The inference time delay is the inference latency on different Runtimes in milliseconds.
CPU is Intel(R) Xeon(R) Gold 6271C, GPU is Tesla T4, TensorRT version 8.4.15, and the fixed CPU thread is 1 for all tests.

README.md

YOLOv7 Quantized Model Deployment

FastDeploy One-Click Model Quantization Tool

Download Quantized YOLOv7 Model

More Detailed Tutorials