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31 lines
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Markdown
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31 lines
1.6 KiB
Markdown
Executable File
# FastDeploy C++ API Summary
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## Runtime
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FastDeploy Runtime can be used as an inference engine with the same code, we can deploy Paddle/ONNX model on different device by different backends.
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Currently, FastDeploy supported backends listed as below,
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| Backend | Hardware | Support Model Format | Platform |
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| :------ | :------- | :------------------- | :------- |
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| Paddle Inference | CPU/Nvidia GPU | Paddle | Windows(x64)/Linux(x64) |
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| ONNX Runtime | CPU/Nvidia GPU | Paddle/ONNX | Windows(x64)/Linux(x64/aarch64)/Mac(x86/arm64) |
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| TensorRT | Nvidia GPU | Paddle/ONNX | Windows(x64)/Linux(x64)/Jetson |
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| OpenVINO | CPU | Paddle/ONNX | Windows(x64)/Linux(x64)/Mac(x86) |
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| Poros | CPU/Nvidia GPU | TorchScript | Linux(x64) |
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### Example code
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- [Python examples](./)
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- [C++ examples](./)
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### Related APIs
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- [RuntimeOption](./structfastdeploy_1_1RuntimeOption.html)
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- [Runtime](./structfastdeploy_1_1Runtime.html)
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## Vision Models
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| Task | Model | API | Example |
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| :---- | :---- | :---- | :----- |
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| object detection | PaddleDetection/PPYOLOE | [fastdeploy::vision::detection::PPYOLOE](./classfastdeploy_1_1vision_1_1detection_1_1PPYOLOE.html) | [C++](./)/[Python](./) |
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| image classification | PaddleClassification serials | [fastdeploy::vision::classification::PaddleClasModel](./classfastdeploy_1_1vision_1_1classification_1_1PaddleClasModel.html) | [C++](./)/[Python](./) |
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| semantic segmentation | PaddleSegmentation serials | [fastdeploy::vision::classification::PaddleSegModel](./classfastdeploy_1_1vision_1_1segmentation_1_1PaddleSegModel.html) | [C++](./)/[Python](./) |
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