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* add GPL lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * add GPL-3.0 lisence * support yolov8 * add pybind for yolov8 * add yolov8 readme Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
47 lines
4.0 KiB
Markdown
Executable File
47 lines
4.0 KiB
Markdown
Executable File
English | [简体中文](README_CN.md)
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# YOLOR Ready-to-deploy Model
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- The YOLOR deployment is based on the code of [YOLOR](https://github.com/WongKinYiu/yolor/releases/tag/weights) and [Pre-trained Model Based on COCO](https://github.com/WongKinYiu/yolor/releases/tag/weights).
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- （1）The *.pt provided by [Official Repository](https://github.com/WongKinYiu/yolor/releases/tag/weights) should [Export the ONNX Model](#Export-the-ONNX-Model) to complete the deployment. The *.pose model’s deployment is not supported；
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- （2）The ScaledYOLOv4 model trained by personal data should [Export the ONNX Model](#%E5%AF%BC%E5%87%BAONNX%E6%A8%A1%E5%9E%8B). Please refer to [Detailed Deployment Documents](#Detailed-Deployment-Documents) to complete the deployment.
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## Export the ONNX Model
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Visit the official [YOLOR](https://github.com/WongKinYiu/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](https://github.com/WongKinYiu/yolor/issues/32) for the solution.
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```bash
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# Download yolor model file
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wget https://github.com/WongKinYiu/yolor/releases/download/weights/yolor-d6-paper-570.pt
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# Export the file in onnx format
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python models/export.py --weights PATH/TO/yolor-xx-xx-xx.pt --img-size 640
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```
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## Download Pre-trained ONNX Model
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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)
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| Model | Size | Accuracy | Note |
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|:---------------------------------------------------------------- |:----- |:----- |:----- |
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| [YOLOR-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-1280-1280.onnx) | 143MB | 54.1% | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-W6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-1280-1280.onnx) | 305MB | 55.5% | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-E6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-1280-1280.onnx ) | 443MB | 56.4% | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-1280-1280.onnx) | 580MB | 57.0% | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-D6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-1280-1280.onnx) | 580MB | 57.3% | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-P6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-640-640.onnx) | 143MB | - | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-W6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-w6-paper-555-640-640.onnx) | 305MB | - | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-E6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-e6-paper-564-640-640.onnx ) | 443MB | - | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-570-640-640.onnx) | 580MB | - | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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| [YOLOR-D6](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-d6-paper-573-640-640.onnx) | 580MB | - | This model file is sourced from [YOLOR](https://github.com/WongKinYiu/yolor)，GPL-3.0 License |
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## Detailed Deployment Documents
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- [Python Deployment](python)
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- [C++ Deployment](cpp)
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## Release Note
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- Document and code are based on [YOLOR weights](https://github.com/WongKinYiu/yolor/releases/tag/weights)
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