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# RKYOLO Python Deployment Example

Two steps before deployment

- 1. Software and hardware should meet the requirements. Refer to [FastDeploy Environment Requirements](../../../../../docs/cn/build_and_install/rknpu2.md)

This directory provides examples that `infer.py` fast finishes the deployment of Picodet on RKNPU. The script is as follows

```bash
# Download the example code for deployment
git clone https://github.com/PaddlePaddle/FastDeploy.git
cd FastDeploy/examples/vision/detection/rkyolo/python

# Download images
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# copy model
cp -r ./model /path/to/FastDeploy/examples/vision/detection/rkyolo/python

# Inference
python3 infer.py --model_file /path/to/model  --image /path/to/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:

```python
model.postprocessor.class_num = 3
```

## Note
The model needs to be in NHWC format on RKNPU. The normalized image will be embedded in the RKNN model. Therefore, when we deploy with FastDeploy, call DisablePermute(C++) or `disable_permute(Python)` to disable normalization and data format conversion during preprocessing.

## Other Documents

- [PaddleDetection Model Description](..)
- [PaddleDetection C++ Deployment](../cpp)
- [model prediction Results](../../../../../docs/api/vision_results/)
- [Convert PaddleDetection RKNN Model Files](../README.md)