Object detection, 3D detection, and pose estimation using center point detection:
Objects as Points,
Xingyi Zhou, Dequan Wang, Philipp Krähenbühl,
CenterNet code
- Strong: 增加支持mobilenetV2,mobilenetV3,efficientdet,shufflenetv2,部分网络需要支持DCNv2.
- 类别: 可支持行人、人脸、车辆、缺陷等检测,只需要修改数据加载即可
Backbone | AP / FPS | Flip AP / FPS | Multi-scale AP / FPS |
---|---|---|---|
Hourglass-104 | 40.3 / 14 | 42.2 / 7.8 | 45.1 / 1.4 |
DLA-34 | 37.4 / 52 | 39.2 / 28 | 41.7 / 4 |
ResNet-101 | 34.6 / 45 | 36.2 / 25 | 39.3 / 4 |
ResNet-18 | 28.1 / 142 | 30.0 / 71 | 33.2 / 12 |
All models and details are available in > CenterNet MODEL_ZOO
- 姿态估计or关键点检测: 修改keypoint的数量及coco加载keypoint的格式可针对性训练多种形式的pose(如landmark等)
Backbone | AP | FPS | TensorRT Speed | Download |
---|---|---|---|---|
DLA-34 | 62.7 | 23 | - | model |
Resnet-50 | 54.5 | 28 | 33 | model |
MobilenetV3 | 46.0 | 30 | 50 | model |
ShuffleNetV2 | 43.9 | 25 | - | model |
High Resolution | 57.1 | 16 | - | model |
HardNet | 45.6 | 30 | - | model |
Darknet53 | 34.2 | 30 | - | model |
centerface与shoulder模型 提取码: 33fj
- centerface: 该版本的centerface是基于修改的centernet训练,训练数据参照widerface,其中对质量不好的face做了过滤,使其更适合人脸识别的工程应用,模型有两个,分别是3.5M和8.9M.
- torch转onnx
python convert2onnx.py
- onnx转TensorRT
python demo_tensorrt.py
- 检测框架支持的TensorRT
If you find this project useful for your research, please use the following BibTeX entry.
@contact{[email protected],
title={Objects as Points},
author={bleakie},
year={2019}
}