PaddleDetection provides multiple deployment forms of Paddle Inference, Paddle Serving and Paddle-Lite, supports multiple platforms such as server, mobile and embedded, and provides a complete Python and C++ deployment solution
## PaddleDetection This section describes the supported deployment modes
| formalization | language | Tutorial | Equipment/Platform |
| Paddle-Lite | C++ | Has perfect | Android、IOS、FPGA、RK... |
## 1.Paddle Inference Deployment
### 1.1 The export model
Use the `tools/export_model.py` script to export the model and the configuration file used during deployment. The configuration file name is `infer_cfg.yml`. The model export script is as follows
The prediction model will be exported to the `output_inference/yolov3_mobilenet_v1_roadsign` directory `infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel`. For details on model export, please refer to the documentation [Tutorial on Paddle Detection MODEL EXPORT](./EXPORT_MODEL_en.md).
### 1.2 Use Paddle Inference to Make Predictions
* Python deployment supports `CPU`, `GPU` and `XPU` environments, Windows, Linux, and NV Jetson embedded devices. Reference Documentation [Python Deployment](python/README.md)
* C++ deployment supports `CPU`, `GPU` and `XPU` environments, Windows and Linux systems, and NV Jetson embedded devices. Reference documentation [C++ deployment](cpp/README.md)
* PaddleDetection supports TensorRT acceleration. Please refer to the documentation for [TensorRT Predictive Deployment Tutorial](TENSOR_RT.md)
**Attention:** Paddle prediction library version requires >=2.1, and batch_size>1 only supports YOLOv3 and PP-YOLO.
## 2.PaddleServing Deployment
### 2.1 Export model
If you want to export the model in `PaddleServing` format, set `export_serving_model=True`:
The prediction model will be exported to the `output_inference/yolov3_darknet53_270e_coco` directory `infer_cfg.yml`, `model.pdiparams`, `model.pdiparams.info`, `model.pdmodel`, `serving_client/` and `serving_server/` folder.
For details on model export, please refer to the documentation [Tutorial on Paddle Detection MODEL EXPORT](./EXPORT_MODEL_en.md).
-[Deploy the PaddleDetection model using PaddleLite](./lite/README.md)
- For details, please refer to [Paddle-Lite-Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo) deployment. For more information, please refer to [Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite)
## 4.Third-Engine deploy(MNN、NCNN、Openvino)
- The Third-Engine deploy take example of PicoDet、TinyPose，the others model is the same
- Suggestion for TinyPose: For Intel CPU Openvino is recommended，for Nvidia GPU PaddleInference is recommended，and for ARM/ANDROID PaddleLite or MNN is recommended.
- 1、Can `Paddle 1.8.4` trained models be deployed with `Paddle2.0`?
Paddle 2.0 is compatible with Paddle 1.8.4, so it is ok. However, some models (such as SOLOv2) use the new OP in Paddle 2.0, which is not allowed.
- 2、When compiling for Windows, the prediction library is compiled with VS2015, will it be a problem to choose VS2017 or VS2019?
For compatibility issues with VS, please refer to: [C++ Visual Studio 2015, 2017 and 2019 binary compatibility](https://docs.microsoft.com/zh-cn/cpp/porting/binary-compat-2015-2017?view=msvc-160)
QA tests show that cuDNN 8 series have memory leakage problems in continuous prediction, and cuDNN 8 performance is worse than cuDNN7. CUDA + cuDNN7.6.4 is recommended for deployment.
