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

examples/vision/classification/paddleclas/quantize/README.md

@@ -0,0 +1,27 @@
+# PaddleClas 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](../../../../../tools/quantization/)
+注意: 推理量化后的分类模型仍然需要FP32模型文件夹下的inference_cls.yaml文件, 自行量化的模型文件夹内不包含此yaml文件, 用户从FP32模型文件夹下复制此yaml文件到量化后的模型文件夹内即可。
+
+## 下载量化完成的PaddleClas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 Top1 | INT8 Top1 |量化方式   |
+| ------------------- | -----------------|-----------|  --------     |--------      |--------      | --------- |-------- |----- |
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar)            | ONNX Runtime         |    CPU    |  86.87        |  59 .32     |      1.46         | 79.12  | 78.87|  离线量化|
+| [ResNet50_vd](https://bj.bcebos.com/paddlehub/fastdeploy/resnet50_vd_ptq.tar)            | TensorRT         |    GPU    |  7.85        |  5.42      |      1.45         | 79.12  | 79.06 | 离线量化 |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/mobilenetv1_ssld_ptq.tar)             | ONNX Runtime |    CPU    |      40.32   |   16.87     |      2.39         |77.89 | 75.09 |离线量化 |
+| [MobileNetV1_ssld](https://bj.bcebos.com/paddlehub/fastdeploy/mobilenetv1_ssld_ptq.tar)             | TensorRT  |    GPU    |      5.10   |   3.35     |      1.52         |77.89 | 76.86 | 离线量化 |
+
+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ImageNet-2012验证集中的图片.
+- 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
+
+- [Python部署](python)
+- [C++部署](cpp)