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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 |
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.