FastDeploy/examples/vision/detection/yolov6/quantize

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YOLOv6 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 YOLOv6s Model

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

Model	Inference Backend	Hardware	FP32  Inference Time Delay	INT8 Inference Time Delay	Acceleration ratio	FP32 mAP	INT8 mAP	Method
YOLOv6s	TensorRT	GPU	8.60	5.16	1.67	42.5	40.6	Quantized distillation training
YOLOv6s	Paddle Inference	CPU	356.62	125.72	2.84	42.5	41.2	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.

More Detailed Tutorials

• Python Deployment
• C++ Deployment