Files
FastDeploy/examples/vision/detection/yolor
charl-u cbf88a46fa [Doc]Update English version of some documents (#1083)
* 第一次提交

* 补充一处漏翻译

* deleted:    docs/en/quantize.md

* Update one translation

* Update en version

* Update one translation in code

* Standardize one writing

* Standardize one writing

* Update some en version

* Fix a grammer problem

* Update en version for api/vision result

* Merge branch 'develop' of https://github.com/charl-u/FastDeploy into develop

* Checkout the link in README in vision_results/ to the en documents

* Modify a title

* Add link to serving/docs/

* Finish translation of demo.md

* Update english version of serving/docs/

* Update title of readme

* Update some links

* Modify a title

* Update some links

* Update en version of java android README

* Modify some titles

* Modify some titles

* Modify some titles

* modify article to document

* update some english version of documents in examples

* Add english version of documents in examples/visions

* Sync to current branch

* Add english version of documents in examples

* Add english version of documents in examples

* Add english version of documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples

* Update some documents in examples
2023-01-09 10:08:19 +08:00
..

English | 简体中文

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 YOLORGPL-3.0 License
YOLOR-W6-1280 305MB 55.5% This model file is sourced from YOLORGPL-3.0 License
YOLOR-E6-1280 443MB 56.4% This model file is sourced from YOLORGPL-3.0 License
YOLOR-D6-1280 580MB 57.0% This model file is sourced from YOLORGPL-3.0 License
YOLOR-D6-1280 580MB 57.3% This model file is sourced from YOLORGPL-3.0 License
YOLOR-P6 143MB - This model file is sourced from YOLORGPL-3.0 License
YOLOR-W6 305MB - This model file is sourced from YOLORGPL-3.0 License
YOLOR-E6 443MB - This model file is sourced from YOLORGPL-3.0 License
YOLOR-D6 580MB - This model file is sourced from YOLORGPL-3.0 License
YOLOR-D6 580MB - This model file is sourced from YOLORGPL-3.0 License

Detailed Deployment Documents

Release Note