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* 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
视觉模型部署
本目录下提供了各类视觉模型的部署,主要涵盖以下任务类型
| 任务类型 | 说明 | 预测结果结构体 |
|---|---|---|
| Detection | 目标检测,输入图像,检测图像中物体位置,并返回检测框坐标及类别和置信度 | DetectionResult |
| Segmentation | 语义分割,输入图像,给出图像中每个像素的分类及置信度 | SegmentationResult |
| Classification | 图像分类,输入图像,给出图像的分类结果和置信度 | ClassifyResult |
| FaceDetection | 人脸检测,输入图像,检测图像中人脸位置,并返回检测框坐标及人脸关键点 | FaceDetectionResult |
| FaceRecognition | 人脸识别,输入图像,返回可用于相似度计算的人脸特征的embedding | FaceRecognitionResult |
| Matting | 抠图,输入图像,返回图片的前景每个像素点的Alpha值 | MattingResult |
| OCR | 文本框检测,分类,文本框内容识别,输入图像,返回文本框坐标,文本框的方向类别以及框内的文本内容 | OCRResult |
FastDeploy API设计
视觉模型具有较有统一任务范式,在设计API时(包括C++/Python),FastDeploy将视觉模型的部署拆分为四个步骤
- 模型加载
- 图像预处理
- 模型推理
- 推理结果后处理
FastDeploy针对飞桨的视觉套件,以及外部热门模型,提供端到端的部署服务,用户只需准备模型,按以下步骤即可完成整个模型的部署
- 加载模型
- 调用
predict接口
FastDeploy在各视觉模型部署时,也支持一键切换后端推理引擎,详情参阅如何切换模型推理引擎。