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Visual Model Deployment

This directory provides the deployment of various visual models, including the following task types

Task Type	Description	Predicted Structure
Detection	Target detection. Input the image, detect the object’s position in the image, and return the detected box coordinates, category, and confidence coefficient	DetectionResult
Segmentation	Semantic segmentation. Input the image and output the classification and confidence coefficient of each pixel	SegmentationResult
Classification	Image classification. Input the image and output the classification result and confidence coefficient of the image	ClassifyResult
FaceDetection	Face detection. Input the image, detect the position of faces in the image, and return detected box coordinates and key points of faces	FaceDetectionResult
FaceAlignment	Face alignment(key points detection).Input the image and return face key points	FaceAlignmentResult
KeypointDetection	Key point detection. Input the image and return the coordinates and confidence coefficient of the key points of the person's behavior in the image	KeyPointDetectionResult
FaceRecognition	Face recognition. Input the image and return an embedding of facial features that can be used for similarity calculation	FaceRecognitionResult
Matting	Matting. Input the image and return the Alpha value of each pixel in the foreground of the image	MattingResult
OCR	Text box detection, classification, and text box content recognition. Input the image and return the text box’s coordinates, orientation category, and content	OCRResult
MOT	Multi-objective tracking. Input the image and detect the position of objects in the image, and return detected box coordinates, object id, and class confidence	MOTResult
HeadPose	Head posture estimation. Return head Euler angle	HeadPoseResult

FastDeploy API Design

Generally, visual models have a uniform task paradigm. When designing API (including C++/Python), FastDeploy conducts four steps to deploy visual models

• Model loading
• Image pre-processing
• Model Inference
• Post-processing of inference results

Targeted at the vision suite of PaddlePaddle and external popular models, FastDeploy provides an end-to-end deployment service. Users merely prepare the model and follow these steps to complete the deployment

• Model Loading
• Calling the predictinterface

When deploying visual models, FastDeploy supports one-click switching of the backend inference engine. Please refer to How to switch model inference engine.