apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-10-05 16:48:03 +08:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity
Files
b0663209f6763fe73a31526bfc5d46fde1daf8d7
FastDeploy/examples/vision/detection/yolov6/quantize
History
yunyaoXYY b0663209f6 Add Examples to deploy quantized models (#342) 
* Add PaddleOCR Support

* Add PaddleOCR Support

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Support

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Add PaddleOCRv3 Support

* Add PaddleOCRv3 Supports

* Add PaddleOCRv3 Suport

* Fix Rec diff

* Remove useless functions

* Remove useless comments

* Add PaddleOCRv2 Support

* Add PaddleOCRv3 & PaddleOCRv2 Support

* remove useless parameters

* Add utils of sorting det boxes

* Fix code naming convention

* Fix code naming convention

* Fix code naming convention

* Fix bug in the Classify process

* Imporve OCR Readme

* Fix diff in Cls model

* Update Model Download Link in Readme

* Fix diff in PPOCRv2

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Improve OCR readme

* Imporve OCR readme

* Improve OCR readme

* Fix conflict

* Add readme for OCRResult

* Improve OCR readme

* Add OCRResult readme

* Improve OCR readme

* Improve OCR readme

* Add Model Quantization Demo

* Fix Model Quantization Readme

* Fix Model Quantization Readme

* Add the function to do PTQ quantization

* Improve quant tools readme

* Improve quant tool readme

* Improve quant tool readme

* Add PaddleInference-GPU for OCR Rec model

* Add QAT method to fastdeploy-quantization tool

* Remove examples/slim for now

* Move configs folder

* Add Quantization Support for Classification Model

* Imporve ways of importing preprocess

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Upload YOLO Benchmark on readme

* Improve Quantization configs and readme

* Add support for multi-inputs model

* Add backends and params file for YOLOv7

* Add quantized model deployment support for YOLO series

* Fix YOLOv5 quantize readme

* Fix YOLO quantize readme

* Fix YOLO quantize readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Improve quantize YOLO readme

* Fix bug, change Fronted to ModelFormat

* Change Fronted to ModelFormat

* Add examples to deploy quantized paddleclas models

* Fix readme

* Add quantize Readme

* Add quantize Readme

* Add quantize Readme

* Modify readme of quantization tools

* Modify readme of quantization tools

* Improve quantization tools readme

* Improve quantization readme

* Improve PaddleClas quantized model deployment  readme

* Add PPYOLOE-l quantized deployment examples

* Improve quantization tools readme
2022-10-14 13:35:45 +08:00
..
cpp
Add Examples to deploy quantized models (#342)
2022-10-14 13:35:45 +08:00
python
Add Examples to deploy quantized models (#342)
2022-10-14 13:35:45 +08:00
README.md
Add Examples to deploy quantized models (#342)
2022-10-14 13:35:45 +08:00

README.md

YOLOv6量化模型部署

FastDeploy已支持部署量化模型,并提供一键模型量化的工具. 用户可以使用一键模型量化工具,自行对模型量化后部署, 也可以直接下载FastDeploy提供的量化模型进行部署.

FastDeploy一键模型量化工具

FastDeploy 提供了一键量化工具, 能够简单地通过输入一个配置文件, 对模型进行量化. 详细教程请见: 一键模型量化工具

下载量化完成的YOLOv6s模型

用户也可以直接下载下表中的量化模型进行部署.

模型 推理后端 部署硬件 FP32推理时延 INT8推理时延 加速比 FP32 mAP INT8 mAP 量化方式
YOLOv6s TensorRT GPU 12.89 8.92 1.45 42.5 40.6 量化蒸馏训练
YOLOv6s Paddle Inference CPU 366.41 131.70 2.78 42.5 41.2 量化蒸馏训练

上表中的数据, 为模型量化前后，在FastDeploy部署的端到端推理性能.

  • 测试图片为COCO val2017中的图片.
  • 推理时延为端到端推理(包含前后处理)的平均时延, 单位是毫秒.
  • CPU为Intel(R) Xeon(R) Gold 6271C, GPU为Tesla T4, TensorRT版本8.4.15, 所有测试中固定CPU线程数为1.

详细部署文档