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[Model] Support YOLOv8 (PaddlePaddle#1137)
* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme Co-authored-by: DefTruth <[email protected]>
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| English | [简体中文](README_CN.md) | ||
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| # YOLOv8 Ready-to-deploy Model | ||
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| - The deployment of the YOLOv8 model is based on [YOLOv8](https://github.com/ultralytics/ultralytics) and [Pre-trained Model Based on COCO](https://github.com/ultralytics/ultralytics) | ||
| - （1）The *.onnx provided by [Official Repository](https://github.com/ultralytics/ultralytics) can be deployed directly； | ||
| - （2）The YOLOv8 model trained by personal data should employ `export.py` in [YOLOv8](https://github.com/ultralytics/ultralytics) to export the ONNX files for deployment. | ||
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| ## Download Pre-trained ONNX Model | ||
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| For developers' testing, models exported by YOLOv8 are provided below. Developers can download them directly. (The accuracy in the following table is derived from the source official repository) | ||
| | Model | Size | Accuracy | Note | | ||
| |:---------------------------------------------------------------- |:----- |:----- |:---- | | ||
| | [YOLOv8n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8n.onnx) | 12.1MB | 37.3% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx) | 42.6MB | 44.9% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8m.onnx) | 98.8MB | 50.2% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8l.onnx) | 166.7MB | 52.9% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8x.onnx) | 260.3MB | 53.9% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
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| ## Detailed Deployment Documents | ||
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| - [Python Deployment](python) | ||
| - [C++ Deployment](cpp) | ||
| - [Serving Deployment](serving) | ||
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| ## Release Note | ||
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| - Document and code are based on [YOLOv8](https://github.com/ultralytics/ultralytics) |
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| [English](README.md) | 简体中文 | ||
| # YOLOv8准备部署模型 | ||
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| - YOLOv8部署模型实现来自[YOLOv8](https://github.com/ultralytics/ultralytics),和[基于COCO的预训练模型](https://github.com/ultralytics/ultralytics) | ||
| - （1）[官方库](https://github.com/ultralytics/ultralytics)提供的*.onnx可直接进行部署； | ||
| - （2）开发者基于自己数据训练的YOLOv8模型，可使用[YOLOv8](https://github.com/ultralytics/ultralytics)中的`export.py`导出ONNX文件后，完成部署。 | ||
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| ## 下载预训练ONNX模型 | ||
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| 为了方便开发者的测试，下面提供了YOLOv8导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库） | ||
| | 模型 | 大小 | 精度 | 备注 | | ||
| |:---------------------------------------------------------------- |:----- |:----- |:---- | | ||
| | [YOLOv8n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8n.onnx) | 12.1MB | 37.3% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx) | 42.6MB | 44.9% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8m](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8m.onnx) | 98.8MB | 50.2% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8l](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8l.onnx) | 166.7MB | 52.9% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
| | [YOLOv8x](https://bj.bcebos.com/paddlehub/fastdeploy/yolov8x.onnx) | 260.3MB | 53.9% | This model file is sourced from [YOLOv8](https://github.com/ultralytics/ultralytics)，GPL-3.0 License | | ||
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| ## 详细部署文档 | ||
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| - [Python部署](python) | ||
| - [C++部署](cpp) | ||
| - [服务化部署](serving) | ||
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| ## 版本说明 | ||
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| - 本版本文档和代码基于[YOLOv8](https://github.com/ultralytics/ultralytics) 编写 |
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| PROJECT(infer_demo C CXX) | ||
| CMAKE_MINIMUM_REQUIRED (VERSION 3.10) | ||
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| # Specify the fastdeploy library path after downloading and decompression | ||
| option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.") | ||
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| include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake) | ||
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| # Add FastDeploy dependent header files | ||
| include_directories(${FASTDEPLOY_INCS}) | ||
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| add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc) | ||
| # Add FastDeploy library dependencies | ||
| target_link_libraries(infer_demo ${FASTDEPLOY_LIBS}) |
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| [English](README.md) | 简体中文 | ||
| # YOLOv8 C++部署示例 | ||
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| 本目录下提供`infer.cc`快速完成YOLOv8在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。 | ||
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| 在部署前，需确认以下两个步骤 | ||
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| - 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) | ||
| - 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md) | ||
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| 以Linux上CPU推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本1.0.3以上(x.x.x>=1.0.3) | ||
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| ```bash | ||
| mkdir build | ||
| cd build | ||
| # 下载 FastDeploy 预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用 | ||
| wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz | ||
| tar xvf fastdeploy-linux-x64-x.x.x.tgz | ||
| cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x | ||
| make -j | ||
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| # 1. 下载官方转换好的 YOLOv8 ONNX 模型文件和测试图片 | ||
| wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx | ||
| wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg | ||
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| # CPU推理 | ||
| ./infer_demo yolov8s.onnx 000000014439.jpg 0 | ||
| # GPU推理 | ||
| ./infer_demo yolov8s.onnx 000000014439.jpg 1 | ||
| # GPU上TensorRT推理 | ||
| ./infer_demo yolov8s.onnx 000000014439.jpg 2 | ||
| ``` | ||
| 运行完成可视化结果如下图所示 | ||
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| <img width="640" src="https://user-images.githubusercontent.com/67993288/184309358-d803347a-8981-44b6-b589-4608021ad0f4.jpg"> | ||
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| 以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考: | ||
| - [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md) | ||
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| 如果用户使用华为昇腾NPU部署, 请参考以下方式在部署前初始化部署环境: | ||
| - [如何使用华为昇腾NPU部署](../../../../../docs/cn/faq/use_sdk_on_ascend.md) | ||
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| ## YOLOv8 C++接口 | ||
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| ### YOLOv8类 | ||
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| ```c++ | ||
| fastdeploy::vision::detection::YOLOv8( | ||
| const string& model_file, | ||
| const string& params_file = "", | ||
| const RuntimeOption& runtime_option = RuntimeOption(), | ||
| const ModelFormat& model_format = ModelFormat::ONNX) | ||
| ``` | ||
| YOLOv8模型加载和初始化，其中model_file为导出的ONNX模型格式。 | ||
| **参数** | ||
| > * **model_file**(str): 模型文件路径 | ||
| > * **params_file**(str): 参数文件路径，当模型格式为ONNX时，此参数传入空字符串即可 | ||
| > * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置 | ||
| > * **model_format**(ModelFormat): 模型格式，默认为ONNX格式 | ||
| #### Predict函数 | ||
| > ```c++ | ||
| > YOLOv8::Predict(cv::Mat* im, DetectionResult* result) | ||
| > ``` | ||
| > | ||
| > 模型预测接口，输入图像直接输出检测结果。 | ||
| > | ||
| > **参数** | ||
| > | ||
| > > * **im**: 输入图像，注意需为HWC，BGR格式 | ||
| > > * **result**: 检测结果，包括检测框，各个框的置信度, DetectionResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/) | ||
| ### 类成员变量 | ||
| #### 预处理参数 | ||
| 用户可按照自己的实际需求，修改下列预处理参数，从而影响最终的推理和部署效果 | ||
| > > * **size**(vector<int>): 通过此参数修改预处理过程中resize的大小，包含两个整型元素，表示[width, height], 默认值为[640, 640] | ||
| > > * **padding_value**(vector<float>): 通过此参数可以修改图片在resize时候做填充(padding)的值, 包含三个浮点型元素, 分别表示三个通道的值, 默认值为[114, 114, 114] | ||
| > > * **is_no_pad**(bool): 通过此参数让图片是否通过填充的方式进行resize, `is_no_pad=ture` 表示不使用填充的方式，默认值为`is_no_pad=false` | ||
| > > * **is_mini_pad**(bool): 通过此参数可以将resize之后图像的宽高这是为最接近`size`成员变量的值, 并且满足填充的像素大小是可以被`stride`成员变量整除的。默认值为`is_mini_pad=false` | ||
| > > * **stride**(int): 配合`stris_mini_pad`成员变量使用, 默认值为`stride=32` | ||
| - [模型介绍](../../) | ||
| - [Python部署](../python) | ||
| - [视觉模型预测结果](../../../../../docs/api/vision_results/) | ||
| - [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md) |
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