mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-04 08:16:42 +08:00

* 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
English | 简体中文
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
Release Note
- Document and code are based on YOLOR weights