https://github.com/vietanhdev/centernet-bdd-data

A fork of Centernet for Berkeley DeepDrive dataset
https://github.com/vietanhdev/centernet-bdd-data

Last synced: 9 months ago
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A fork of Centernet for Berkeley DeepDrive dataset

• Host: GitHub
• URL: https://github.com/vietanhdev/centernet-bdd-data
• Owner: vietanhdev
• License: mit
• Created: 2020-11-15T12:13:45.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-11-15T12:38:18.000Z (over 5 years ago)
• Last Synced: 2025-04-09T13:04:05.148Z (about 1 year ago)
• Language: Python
• Size: 6.13 MB
• Stars: 4
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Objects as Points - For BDD dataset

Object detection, 3D detection, and pose estimation using center point detection:

![](readme/fig2.png)

> [**Objects as Points**](http://arxiv.org/abs/1904.07850),            

> Xingyi Zhou, Dequan Wang, Philipp Krähenbühl,        

> *arXiv technical report ([arXiv 1904.07850](http://arxiv.org/abs/1904.07850))*         

This project is based on original CenterNet - Objects as Points from [https://github.com/xingyizhou/CenterNet](https://github.com/xingyizhou/CenterNet). I added some customizations to train CenterNet on [BDD100k dataset](https://bdd-data.berkeley.edu/) for my advanced driver-assistance system project. The detailed training result can be found at [this blog post](https://aicurious.io/posts/adas-jetson-nano-intro-and-hardware/).

Training instruction and be found at [original repository](https://github.com/xingyizhou/CenterNet).

## License

CenterNet itself is released under the MIT License (refer to the LICENSE file for details).

Portions of the code are borrowed from [human-pose-estimation.pytorch](https://github.com/Microsoft/human-pose-estimation.pytorch) (image transform, resnet), [CornerNet](https://github.com/princeton-vl/CornerNet) (hourglassnet, loss functions), [dla](https://github.com/ucbdrive/dla) (DLA network), [DCNv2](https://github.com/CharlesShang/DCNv2)(deformable convolutions), [tf-faster-rcnn](https://github.com/endernewton/tf-faster-rcnn)(Pascal VOC evaluation) and [kitti_eval](https://github.com/prclibo/kitti_eval) (KITTI dataset evaluation). Please refer to the original License of these projects (See [NOTICE](NOTICE)).

## Citation

If you find this project useful for your research, please use the following BibTeX entry.

    @inproceedings{zhou2019objects,

      title={Objects as Points},

      author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp},

      booktitle={arXiv preprint arXiv:1904.07850},

      year={2019}

    }