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YOLOR Ready-to-deploy Model

Export the ONNX Model

Visit the official YOLOR github repository, follow the guidelines to download the yolor.pt model, and employ models/export.py to get the file in onnx format. If the exported onnx model has a substandard accuracy or other problems about data dimension, you can refer to yolor#32 for the solution.

# Download yolor model file
wget https://github.com/WongKinYiu/yolor/releases/download/weights/yolor-d6-paper-570.pt

# Export the file in onnx format
python models/export.py  --weights PATH/TO/yolor-xx-xx-xx.pt --img-size 640

Download Pre-trained ONNX Model

For developers' testing, models exported by YOLOR are provided below. Developers can download them directly. (The accuracy in the following table is derived from the source official repository)

Model Size Accuracy Note
YOLOR-P6-1280 143MB 54.1% This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-W6-1280 305MB 55.5% This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-E6-1280 443MB 56.4% This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-D6-1280 580MB 57.0% This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-D6-1280 580MB 57.3% This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-P6 143MB - This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-W6 305MB - This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-E6 443MB - This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-D6 580MB - This model file is sourced from YOLOR,GPL-3.0 License
YOLOR-D6 580MB - This model file is sourced from YOLOR,GPL-3.0 License

Detailed Deployment Documents

Release Note