FastDeploy/examples/vision/detection/paddledetection/quantize

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PaddleDetection Quantification Model Deployment

FastDeploy supports the deployment of quantification models and provides a convenient tool for automatic model compression. Users can use it to deploy models after quantification or directly deploy quantized models provided by FastDeploy.

FastDeploy one-click model auto-compression tool

FastDeploy provides a one-click auto-compression tool that allows users to quantize models by simply entering a configuration file.
Refer to one-click auto-compression tool for details.

Download the quantized PP-YOLOE-l model

Users can also directly download the quantized models in the table below. (Click the model name to download it)

Benchmark table description:

• Runtime latency: model’s inference latency on multiple Runtimes, including CPU->GPU data copy, GPU inference, and GPU->CPU data copy time. It does not include the pre and post processing time of the model.
• End2End latency: model’s latency in the actual inference scenario, including the pre and post processing time of the model.
• Measured latency: The average latency after 1000 times of inference in milliseconds.
• INT8 + FP16: Enable FP16 inference for Runtime while inferring the INT8 quantification model
• INT8 + FP16 + PM: Use Pinned Memory to speed up the GPU->CPU data copy while inferring the INT8 quantization model with FP16 turned on.
• Maximum speedup ratio: Obtained by dividing the FP32 latency by the highest INT8 inference latency.
• The strategy is to use a few unlabeled data sets to train the model for quantification and to verify the accuracy on the full validation set. The INT8 accuracy does not represent the highest value.
• The CPU is Intel(R) Xeon(R) Gold 6271C, , and the number of CPU threads is fixed to 1. The GPU is Tesla T4 with TensorRT version 8.4.15.

Runtime Benchmark

Model	Inference Backend	Deployment Hardware	FP32 Runtime Latency	INT8 Runtime Latency	INT8 + FP16 Runtime Latency	INT8+FP16+PM Runtime Latency	Maximum Speedup Ratio	FP32 mAP	INT8 mAP	Quantification Method
ppyoloe_crn_l_300e_coco	TensorRT	GPU	27.90	6.39	6.44	5.95	4.67	51.4	50.7	Quantized distillation training
ppyoloe_crn_l_300e_coco	Paddle-TensorRT	GPU	30.89	None	13.78	14.01	2.24	51.4	50.5	Quantized distillation training
ppyoloe_crn_l_300e_coco	ONNX Runtime	CPU	1057.82	449.52	None	None	2.35	51.4	50.0	Quantized distillation training

NOTE:

• The reason why TensorRT is faster than Paddle-TensorRT is that the multiclass_nms3 operator is removed during runtime

End2End Benchmark

Model	Inference Backend	Deployment Hardware	FP32 End2End Latency	INT8 End2End Latency	INT8 + FP16 End2End Latency	INT8+FP16+PM End2End Latency	Maximum Speedup Ratio	FP32 mAP	INT8 mAP	Quantification Method
ppyoloe_crn_l_300e_coco	TensorRT	GPU	35.75	15.42	20.70	20.85	2.32	51.4	50.7	Quantized distillation training
ppyoloe_crn_l_300e_coco	Paddle-TensorRT	GPU	33.48	None	18.47	18.03	1.81	51.4	50.5	Quantized distillation training
ppyoloe_crn_l_300e_coco	ONNX Runtime	CPU	1067.17	461.037	None	None	2.31	51.4	50.0	Quantized distillation training

Detailed Deployment Tutorials

• Python Deployment
• C++ Deployment