OrgLion-ML
/
CenterNet
mirror of https://github.com/xingyizhou/CenterNet.git
1
0
Fork 0
Code Issues Releases Wiki Activity

add links

This commit is contained in:
Xingyi Zhou 2020-06-21 19:53:44 -05:00
parent 1085662179
commit 2b7692c377
1 changed files with 11 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
README.md
View File

@ -8,6 +8,11 @@ Object detection, 3D detection, and pose estimation using center point detection
Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed! Contact: [zhouxy@cs.utexas.edu](mailto:zhouxy@cs.utexas.edu). Any questions or discussions are welcomed!
## Updates
- (June, 2020) We released a state-of-the-art Lidar-based 3D detection and tracking framework [CenterPoint](https://github.com/tianweiy/CenterPoint).
- (April, 2020) We released a state-of-the-art (multi-category-/ pose-/ 3d-) tracking extension [CenterTrack](https://github.com/xingyizhou/CenterTrack).
## Abstract ## Abstract
Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point -- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time. Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point -- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.
@ -121,10 +126,16 @@ If you are interested in training CenterNet in a new dataset, use CenterNet in a
## Third-party resources ## Third-party resources
- CenterNet + embedding learning based tracking: [FairMOT](https://github.com/ifzhang/FairMOT) from [Yifu Zhang](https://github.com/ifzhang).
- Detectron2 based implementation: [CenterNet-better](https://github.com/FateScript/CenterNet-better) from [Feng Wang](https://github.com/FateScript).
- Keras Implementation: [keras-centernet](https://github.com/see--/keras-centernet) from [see--](https://github.com/see--) and [keras-CenterNet](https://github.com/xuannianz/keras-CenterNet) from [xuannianz](https://github.com/xuannianz). - Keras Implementation: [keras-centernet](https://github.com/see--/keras-centernet) from [see--](https://github.com/see--) and [keras-CenterNet](https://github.com/xuannianz/keras-CenterNet) from [xuannianz](https://github.com/xuannianz).
- MXnet implementation: [mxnet-centernet](https://github.com/Guanghan/mxnet-centernet) from [Guanghan Ning](https://github.com/Guanghan).
- Stronger human open estimation models: [centerpose](https://github.com/tensorboy/centerpose) from [tensorboy](https://github.com/tensorboy).
- TensorRT extension with ONNX models: [TensorRT-CenterNet](https://github.com/CaoWGG/TensorRT-CenterNet) from [Wengang Cao](https://github.com/CaoWGG).
- CenterNet + DeepSORT tracking implementation: [centerNet-deep-sort](https://github.com/kimyoon-young/centerNet-deep-sort) from [kimyoon-young](https://github.com/kimyoon-young). - CenterNet + DeepSORT tracking implementation: [centerNet-deep-sort](https://github.com/kimyoon-young/centerNet-deep-sort) from [kimyoon-young](https://github.com/kimyoon-young).
- Blogs on training CenterNet on custom datasets (in Chinese): [ships](https://blog.csdn.net/weixin_42634342/article/details/97756458) from [Rhett Chen](https://blog.csdn.net/weixin_42634342) and [faces](https://blog.csdn.net/weixin_41765699/article/details/100118353) from [linbior](https://me.csdn.net/weixin_41765699). - Blogs on training CenterNet on custom datasets (in Chinese): [ships](https://blog.csdn.net/weixin_42634342/article/details/97756458) from [Rhett Chen](https://blog.csdn.net/weixin_42634342) and [faces](https://blog.csdn.net/weixin_41765699/article/details/100118353) from [linbior](https://me.csdn.net/weixin_41765699).
## License ## License
CenterNet itself is released under the MIT License (refer to the LICENSE file for details). CenterNet itself is released under the MIT License (refer to the LICENSE file for details).