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FastDeploy/examples/vision/detection/yolov7
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English | 简体中文

YOLOv7 Prepare the model for 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, then refer to the example of `yolov7end2end_ort` or `yolov7end2end_ort`
python models/export.py --grid --dynamic --end2end --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 Note
YOLOv7 141MB 51.4% This model file comes from YOLOv7, GPL-3.0 License
YOLOv7x 273MB 53.1% This model file comes from YOLOv7, GPL-3.0 License
YOLOv7-w6 269MB 54.9% This model file comes from YOLOv7, GPL-3.0 License
YOLOv7-e6 372MB 56.0% This model file comes from YOLOv7, GPL-3.0 License
YOLOv7-d6 511MB 56.6% This model file comes from YOLOv7, GPL-3.0 License
YOLOv7-e6e 579MB 56.8% This model file comes from YOLOv7, GPL-3.0 License

Detailed Deployment Tutorials

Version

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