mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 17:17:14 +08:00
33 lines
1.2 KiB
Markdown
33 lines
1.2 KiB
Markdown
English | [简体中文](README_CN.md)
|
|
# RKYOLO C++ Deployment Example
|
|
|
|
This directory provides examples that `infer_xxxxx.cc` fast finishes the deployment of RKYOLO models on Rockchip board through 2-nd generation NPU
|
|
|
|
Two steps before deployment
|
|
|
|
1. Software and hardware should meet the requirements.
|
|
2. Download the precompiled deployment library or deploy FastDeploy repository from scratch according to your development environment.
|
|
|
|
Refer to [RK2 generation NPU deployment repository compilation](../../../../../docs/cn/build_and_install/rknpu2.md)
|
|
|
|
```bash
|
|
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
|
|
cd build
|
|
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
|
|
make -j8
|
|
./infer_rkyolo /path/to/model 000000014439.jpg
|
|
```
|
|
|
|
## common problem
|
|
|
|
If you use the YOLOv5 model you have trained, you may encounter the problem of 'segmentation fault' after running the demo of FastDeploy. It is likely that the number of labels is inconsistent. You can use the following solution:
|
|
|
|
```c++
|
|
model.GetPostprocessor().SetClassNum(3);
|
|
```
|
|
|
|
|
|
- [Model Description](../../)
|
|
- [Python Deployment](../python)
|
|
- [Vision Model Prediction Results](../../../../../docs/api/vision_results/)
|