FastDeploy/examples/vision/detection/yolor

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YOLOR Ready-to-deploy Model

• The YOLOR deployment is based on the code of YOLOR and Pre-trained Model Based on COCO.

  • （1）The *.pt provided by Official Repository should Export the ONNX Model to complete the deployment. The *.pose model’s deployment is not supported；
  • （2）The ScaledYOLOv4 model trained by personal data should Export the ONNX Model. Please refer to Detailed Deployment Documents to complete the deployment.

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

• Python Deployment
• C++ Deployment

Release Note

• Document and code are based on YOLOR weights