Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/hukaixuan19970627/yolov5_obb

yolov5 + csl_label.(Oriented Object Detection)（Rotation Detection）（Rotated BBox）基于yolov5的旋转目标检测
https://github.com/hukaixuan19970627/yolov5_obb

aerial-imagery detection dota oriented rotated-object rotation yolov5

Last synced: 6 days ago
JSON representation

yolov5 + csl_label.(Oriented Object Detection)（Rotation Detection）（Rotated BBox）基于yolov5的旋转目标检测

Host: GitHub
URL: https://github.com/hukaixuan19970627/yolov5_obb
Owner: hukaixuan19970627
License: gpl-3.0
Created: 2021-03-17T09:13:16.000Z (almost 4 years ago)
Default Branch: master
Last Pushed: 2023-10-13T19:46:15.000Z (over 1 year ago)
Last Synced: 2025-02-08T13:03:23.326Z (13 days ago)
Topics: aerial-imagery, detection, dota, oriented, rotated-object, rotation, yolov5
Language: Python
Homepage:
Size: 17.7 MB
Stars: 1,872
Watchers: 12
Forks: 429
Open Issues: 191
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE

Awesome Lists containing this project

awesome-yolo-object-detection - hukaixuan19970627/yolov5_obb
awesome-yolo-object-detection - hukaixuan19970627/yolov5_obb

README

        # Yolov5 for Oriented Object Detection

![图片](./docs/detection.png)

![train_batch0.jpg](./docs/train_batch6.jpg)

![results.png](./docs/results.png)

The code for the implementation of “[Yolov5](https://github.com/ultralytics/yolov5) + [Circular Smooth Label](https://arxiv.org/abs/2003.05597v2)”. 

# Results and Models

The results on **DOTA_subsize1024_gap200_rate1.0** test-dev set are shown in the table below. (**password: yolo**)

 |Model
^{(download link) |Size
^{(pixels) | TTA
^{(multi-scale/
rotate testing) | OBB mAP^{test
^{0.5
DOTAv1.0 | OBB mAP^{test
^{0.5
DOTAv1.5 | OBB mAP^{test
^{0.5
DOTAv2.0 | Speed
^{CPU b1
(ms)|Speed
^{2080Ti b1
(ms) |Speed
^{2080Ti b16
(ms) |params
^{(M) |FLOPs
^{@640 (B) 

 | ----                                                                                                                                                           | ---  | ---   | ---      | ---   | ---   | ---   | ---   | --- | --- | ---

 |yolov5m [[baidu](https://pan.baidu.com/s/1UPNaMuQ_gNce9167FZx8-w)/[google](https://drive.google.com/file/d/1NMgxcN98cmBg9_nVK4axxqfiq4pYh-as/view?usp=sharing)]  |1024  | ×     |**77.3** |**73.2** |**58.0**  |**328.2**      |**16.9**     |**11.3**      |**21.6**   |**50.5**   

 |yolov5s [[baidu](https://pan.baidu.com/s/1Lqw42xlSZxZn-2gNniBpmw?pwd=yolo)]    |1024  | ×     |**76.8**   |-      |-      |-      |**15.6**  | -     |**7.5**     |**17.5**    

 |yolov5n [[baidu](https://pan.baidu.com/s/1Lqw42xlSZxZn-2gNniBpmw?pwd=yolo)]    |1024  | ×     |**73.3**   |-      |-      |-      |**15.2**  | -     |**2.0**     |**5.0**}}}}}}}}}}}}}}

  Table Notes (click to expand / **点我看更多**)

* All checkpoints are trained to 300 epochs with [COCO pre-trained checkpoints](https://github.com/ultralytics/yolov5/releases/tag/v6.0), default settings and hyperparameters.

* **mAP^{test dota}** values are for single-model single-scale on [DOTA](https://captain-whu.github.io/DOTA/index.html)(1024,1024,200,1.0) dataset.
Reproduce Example:

 ```shell

 python val.py --data 'data/dotav15_poly.yaml' --img 1024 --conf 0.01 --iou 0.4 --task 'test' --batch 16 --save-json --name 'dotav15_test_split'

 python tools/TestJson2VocClassTxt.py --json_path 'runs/val/dotav15_test_split/best_obb_predictions.json' --save_path 'runs/val/dotav15_test_split/obb_predictions_Txt'

 python DOTA_devkit/ResultMerge_multi_process.py --scrpath 'runs/val/dotav15_test_split/obb_predictions_Txt' --dstpath 'runs/val/dotav15_test_split/obb_predictions_Txt_Merged'

 zip the poly format results files and submit it to https://captain-whu.github.io/DOTA/evaluation.html

 ```

* **Speed** averaged over DOTAv1.5 val_split_subsize1024_gap200 images using a 2080Ti gpu. NMS + pre-process times is included.
Reproduce by `python val.py --data 'data/dotav15_poly.yaml' --img 1024 --task speed --batch 1`

# [Updates](./docs/ChangeLog.md)

- [2022/1/7] : **Faster and stronger**, some bugs fixed, yolov5 base version updated.

# Installation

Please refer to [install.md](./docs/install.md) for installation and dataset preparation.

# Getting Started 

This repo is based on [yolov5](https://github.com/ultralytics/yolov5). 

And this repo has been rebuilt, Please see [GetStart.md](./docs/GetStart.md) for the Oriented Detection latest basic usage.

#  Acknowledgements

I have used utility functions from other wonderful open-source projects. Espeicially thank the authors of:

* [ultralytics/yolov5](https://github.com/ultralytics/yolov5).

* [Thinklab-SJTU/CSL_RetinaNet_Tensorflow](https://github.com/Thinklab-SJTU/CSL_RetinaNet_Tensorflow).

* [jbwang1997/OBBDetection](https://github.com/jbwang1997/OBBDetection)

* [CAPTAIN-WHU/DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit)

## More detailed explanation

想要了解相关实现的细节和原理可以看我的知乎文章:   

* [自己改建YOLOv5旋转目标的踩坑记录](https://www.zhihu.com/column/c_1358464959123390464).

## 有问题反馈

在使用中有任何问题，建议先按照[install.md](./docs/install.md)检查环境依赖项，再按照[GetStart.md](./docs/GetStart.md)检查使用流程是否正确，善用搜索引擎和github中的issue搜索框，可以极大程度上节省你的时间。

若遇到的是新问题，可以用以下联系方式跟我交流，为了提高沟通效率，请尽可能地提供相关信息以便我复现该问题。

* 知乎（@[略略略](https://www.zhihu.com/people/lue-lue-lue-3-92-86)）

* 代码问题提issues,其他问题请知乎上联系

## 关于作者

```javascript

  Name  : "胡凯旋"

  describe myself："咸鱼一枚"