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91 lines
3.6 KiB
Markdown
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91 lines
3.6 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# YOLOv5Cls C++ Deployment Example
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This directory provides examples that ` infer.cc` fast finishes the deployment of YOLOv5Cls models on CPU/GPU and GPU accelerated by TensorRT.
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Before deployment, two steps require confirmation
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../../docs/en/build_and_install/download_prebuilt_libraries.md).
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Taking CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
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```bash
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mkdir build
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cd build
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# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# Download the official converted yolov5 model file and test images
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wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-cls.onnx
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# CPU inference
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./infer_demo yolov5n-cls.onnx 000000014439.jpg 0
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# GPU inference
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./infer_demo yolov5n-cls.onnx 000000014439.jpg 1
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# TensorRT Inference on GPU
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./infer_demo yolov5n-cls.onnx 000000014439.jpg 2
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```
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The result returned after running is as follows
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```bash
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ClassifyResult(
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label_ids: 265,
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scores: 0.196327,
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)
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```
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The above command works for Linux or MacOS. Refer to:
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- [How to use FastDeploy C++ SDK in Windows](../../../../../docs/en/faq/use_sdk_on_windows.md) for SDK use-pattern in Windows.
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## YOLOv5Cls C++ Interface
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### YOLOv5Cls Class
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```c++
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fastdeploy::vision::classification::YOLOv5Cls(
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const string& model_file,
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const string& params_file = "",
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::ONNX)
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```
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YOLOv5Cls model loading and initialization, among which model_file is the exported ONNX model format
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**Parameter**
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> * **model_file**(str): Model file path
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> * **params_file**(str): Parameter file path. Only passing an empty string when the model is in ONNX format
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> * **runtime_option**(RuntimeOption): Backend inference configuration. None by default. (use the default configuration)
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> * **model_format**(ModelFormat): Model format. ONNX format by default
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#### Predict Function
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> ```c++
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> YOLOv5Cls::Predict(cv::Mat* im, int topk = 1)
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> ```
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>
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> Model prediction interface. Input images and output classification topk results directly.
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>
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> **Parameter**
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>
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> > * **input_image**(np.ndarray): Input data in HWC or BGR format
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> > * **topk**(int): Return the topk classification results with the highest prediction probability. Default 1
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> **Return**
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>
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> > Return `fastdeploy.vision.ClassifyResult` structure. Refer to [Vision Model Prediction Results](../../../../../docs/api/vision_results/) for the description of the structure.
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## Other Documents
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- [YOLOv5Cls Model Description](..)
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- [YOLOv5Cls Python Deployment](../python)
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- [Model Prediction Results](../../../../../docs/api/vision_results/)
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- [How to switch the model inference backend engine](../../../../../docs/en/faq/how_to_change_backend.md)
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