# PaddleDetection Model Export as ONNX Format Tutorial PaddleDetection Model support is saved in ONNX format and the list of current test support is as follows | Model | OP Version | NOTE | | :---- | :----- | :--- | | YOLOv3 | 11 | Only batch=1 inferring is supported. Model export needs fixed shape | | PP-YOLO | 11 | Only batch=1 inferring is supported. A MatrixNMS will be converted to an NMS with slightly different precision; Model export needs fixed shape | | PP-YOLOv2 | 11 | Only batch=1 inferring is supported. MatrixNMS will be converted to NMS with slightly different precision; Model export needs fixed shape | | PP-YOLO Tiny | 11 | Only batch=1 inferring is supported. Model export needs fixed shape | | PP-YOLOE | 11 | Only batch=1 inferring is supported. Model export needs fixed shape | | PP-PicoDet | 11 | Only batch=1 inferring is supported. Model export needs fixed shape | | FCOS | 11 |Only batch=1 inferring is supported | | PAFNet | 11 |- | | TTFNet | 11 |-| | SSD | 11 |Only batch=1 inferring is supported | | PP-TinyPose | 11 | - | | Faster RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7| | Mask RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7| | Cascade RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7| | Cascade Mask RCNN | 16 | Only batch=1 inferring is supported, require paddle2onnx>=0.9.7| The function of saving ONNX is provided by [Paddle2ONNX](https://github.com/PaddlePaddle/Paddle2ONNX). If there is feedback on related problems during conversion, Communicate with engineers in Paddle2ONNX's Github project via [ISSUE](https://github.com/PaddlePaddle/Paddle2ONNX/issues). ## Export Tutorial ### Step 1. Export the Paddle deployment model Export procedure reference document[Tutorial on PaddleDetection deployment model export](./EXPORT_MODEL_en.md), for example: - Models except RCNN series, take YOLOv3 as example ``` cd PaddleDetection python tools/export_model.py -c configs/yolov3/yolov3_darknet53_270e_coco.yml \ -o weights=https://paddledet.bj.bcebos.com/models/yolov3_darknet53_270e_coco.pdparams \ TestReader.inputs_def.image_shape=[3,608,608] \ --output_dir inference_model ``` The derived models were saved in `inference_model/yolov3_darknet53_270e_coco/`, with the structure as follows ``` yolov3_darknet ├── infer_cfg.yml # Model configuration file information ├── model.pdiparams # Static diagram model parameters ├── model.pdiparams.info # Parameter Information is not required └── model.pdmodel # Static diagram model file ``` > check`TestReader.inputs_def.image_shape`, For YOLO series models, specify this parameter when exporting; otherwise, the conversion fails - RCNN series models, take Faster RCNN as example The conditional block needs to be removed in RCNN series when export ONNX model. Add `export_onnx=True` in command line ``` cd PaddleDetection python tools/export_model.py -c configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.yml \ -o weights=https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_1x_coco.pdparams \ export_onnx=True \ --output_dir inference_model ``` The derived models were saved in `inference_model/faster_rcnn_r50_fpn_1x_coco/`, with the structure as follows ``` faster_rcnn_r50_fpn_1x_coco ├── infer_cfg.yml # Model configuration file information ├── model.pdiparams # Static diagram model parameters ├── model.pdiparams.info # Parameter Information is not required └── model.pdmodel # Static diagram model file ``` ### Step 2. Convert the deployment model to ONNX format Install Paddle2ONNX (version 0.9.7 or higher) ``` pip install paddle2onnx ``` Use the following command to convert ``` # YOLOv3 paddle2onnx --model_dir inference_model/yolov3_darknet53_270e_coco \ --model_filename model.pdmodel \ --params_filename model.pdiparams \ --opset_version 11 \ --save_file yolov3.onnx # Faster RCNN paddle2onnx --model_dir inference_model/faster_rcnn_r50_fpn_1x_coco \ --model_filename model.pdmodel \ --params_filename model.pdiparams \ --opset_version 16 \ --save_file faster_rcnn.onnx ``` The transformed model is under the current path`yolov3.onnx` and `faster_rcnn.onnx`