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37 lines
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Markdown
Executable File
English | [简体中文](README_CN.md)
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# Visual Model Deployment
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This directory provides the deployment of various visual models, including the following task types
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| Task Type | Description | Predicted Structure |
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|:-------------- |:----------------------------------- |:-------------------------------------------------------------------------------- |
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| 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](../../docs/api/vision_results/detection_result.md) |
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| Segmentation | Semantic segmentation. Input the image and output the classification and confidence coefficient of each pixel | [SegmentationResult](../../docs/api/vision_results/segmentation_result.md) |
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| Classification | Image classification. Input the image and output the classification result and confidence coefficient of the image | [ClassifyResult](../../docs/api/vision_results/classification_result.md) |
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| 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](../../docs/api/vision_results/face_detection_result.md) |
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| FaceAlignment | Face alignment(key points detection).Input the image and return face key points | [FaceAlignmentResult](../../docs/api/vision_results/face_alignment_result.md) |
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| 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](../../docs/api/vision_results/keypointdetection_result.md) |
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| FaceRecognition | Face recognition. Input the image and return an embedding of facial features that can be used for similarity calculation | [FaceRecognitionResult](../../docs/api/vision_results/face_recognition_result.md) |
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| Matting | Matting. Input the image and return the Alpha value of each pixel in the foreground of the image | [MattingResult](../../docs/api/vision_results/matting_result.md) |
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| 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](../../docs/api/vision_results/ocr_result.md) |
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| 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](../../docs/api/vision_results/mot_result.md) |
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| HeadPose | Head posture estimation. Return head Euler angle | [HeadPoseResult](../../docs/api/vision_results/headpose_result.md) |
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## FastDeploy API Design
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Generally, visual models have a uniform task paradigm. When designing API (including C++/Python), FastDeploy conducts four steps to deploy visual models
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- Model loading
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- Image pre-processing
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- Model Inference
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- Post-processing of inference results
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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
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- Model Loading
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- Calling the `predict`interface
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When deploying visual models, FastDeploy supports one-click switching of the backend inference engine. Please refer to [How to switch model inference engine](../../docs/en/faq/how_to_change_backend.md).
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