FastDeploy/examples/vision/detection/yolov7

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YOLOv7 Prepare the model for Deployment

• YOLOv7 deployment is based on YOLOv7 branching code, and COCO Pre-Trained Models.

  • （1）The *.pt provided by the Official Library can be deployed after the [export ONNX model](#export ONNX model) operation; *.trt and *.pose models do not support deployment.
  • （2）As for YOLOv7 model trained on customized data, please follow the operations guidelines in Export ONNX model and then refer to Detailed Deployment Tutorials to complete the deployment.

Export ONNX Model

# Download yolov7 model file
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt

# Export onnx file (Tips: in accordance with YOLOv7 release v0.1 code)
python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt

# If your code supports exporting ONNX files with NMS, please use the following command to export ONNX files (do not use "--end2end" for now. We will support deployment of ONNX models with NMS in the future)
python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt

Download the pre-trained ONNX model

To facilitate testing for developers, we provide below the models exported by YOLOv7, which developers can download and use directly. (The accuracy of the models in the table is sourced from the official library)

Model	Size	Accuracy
YOLOv7	141MB	51.4%
YOLOv7x	273MB	53.1%
YOLOv7-w6	269MB	54.9%
YOLOv7-e6	372MB	56.0%
YOLOv7-d6	511MB	56.6%
YOLOv7-e6e	579MB	56.8%

Detailed Deployment Tutorials

• Python Deployment
• C++ Deployment

Version

• This tutorial and related code are written based on YOLOv7 0.1